Reviewerly/exHarmony huggingface dataset:
Communicative Functions of Hashtags: Review -

As the use of the hashtag increased in popularity, other social platforms have adapted their use. It's hard to argue against the simplicity and effectiveness of the hashtag. It is easily created and searchable and has become an incredible marketing tool for companies, corporations, celebrities and even the average person. Notwithstanding the early use of hashtags symbol by way of resources for categorization and combining info on a theme-by-theme basis, societal television is presently witnessing a variety of usages that vary as of the original perseverance. The aim of this paper is to analyses the communication functions of hashtags via a blended research paper, field work and research methodology method. Various implementations of hashtags also undergone semantical analysis to demonstrate generalizable patterns. This has established ten contact functions: thematic branding, grouping, socialization, justification, irony, database distribution, speech, campaign creation, advertisement, and brand messaging. The following have also been established: These findings will boost the comprehension of current online communication and demonstrate whether hashtags have become an important part of the discussion. The small scope of this analysis is a constraint.
College English Teaching under the Internet Plus Background

The development and promotion of Internet emerging technology has brought incomparable practical significance and new development ecology for College English education industry. As a significant part of College English teaching. University English audio-visual-oral course undertakes the significant task of strengthening English listening and speaking ability, broadening international vision and improving intercultural communication ability. Due to the “Internet plus”, the education pattern of College English reading, writing, oral English and listening can adopt a blended teaching mode to boost the on-and offline education of audio-visual and intelligent education. The paper first analyzes the common problems in the traditional college and university English language education mode, and puts forward the concept of blended teaching mode, and concludes the path and measures of the reform of College English language teaching mode under the Internet Plus background.
Web GIS-Based Water Distribution Network Management using Google Maps

Geographic Information System is a set of tools made up of hardware, software, data, and users, which allows us to capture, store, manage and analyze digital information, as well as make graphs and maps, and represent alphanumeric data that can help improve civilization to understanding their infrastructures. Water distribution network is an infrastructure that crucial to ensure the population gets the water they needed. In this study, the objective was to make an information system of a water distribution network with computerized documentation that will make it easier to develop and expand for the upcoming population that will use this service. Information system key features will be able to: draw pipelines, legend positions, and water installation connections on the map; and make a draft budget based on water distribution network data in the information system.
Chemical literacy as a component of the natural scientific literacy of students

The article is devoted to the problem of the formation of chemical literacy of students of an educational school. It is shown that chemical literacy is a component of natural science literacy, which is considered as a manifestation of functional literacy. The article analyzes the content of the tasks of the international research of natural science literacy PISA from the open bank of tasks. It was found that the tasks are dominated by the context associated with the content of the sciences of physical systems – physics, geography, geology, but practically no content related to chemistry is presented. The literature contains a sufficient number of individual publications that describe different methods of forming chemical literacy. PISA tasks are used as a tool for the formation and assessment of the formation of chemical literacy. But there are no guidelines for chemistry teachers, including a description of the technology for developing tasks in the PISA format and sets of such tasks. As an innovative component, a methodology for preparing tasks in chemistry in the PISA format within the framework of the systemic and competency-based approaches is proposed.
Innovation of Traditional Culture in English Education Based on Natural Spelling Teaching

With the continuous development of the times, China’s status has been continuously improved on the international stage, and Chinese traditional culture has gradually shown its unique charm in the world. This paper mainly discusses the innovation of traditional culture in English education based on natural spelling teaching, starting with a brief introduction to the concept of natural spelling and its significance in English learning, and mainly discusses the application strategy of traditional culture in English education.
Flexible Projection Search Using Optimal Re-weighted Adjacency for Unsupervised Manifold Learning

Graph-based manifold learning plays an important role in clustering and classification tasks. Regarding the unsupervised case, the local structure of each sample is vital to the quality of clustering results. Many state-of-the-art methods seek the low-dimensional projection matrix for graph embedding, ignoring the contortion of original local structure in the projected space, which impairs the quality of clustering. To remedy this shortcoming, we propose an iterative leaning approach in this paper, aiming at preserving the original locality in each iteration. During iterative steps, adjacency weights of each currently projected sample are optimally defined by quadratic programming with equality constraints, and in return the upcoming projection matrix that attempts to keep the original locality is flexibly determined based on the current weights. Such iteration proceeds until the objective function value converges. In particular, the proposed approach requires very few parameters, leading to simple operation in experiments. Further, local distances of projected samples could be directly obtained from their inner products, with no explicit calculation of projection matrix in each iteration. We repeat k-means clustering many times w.r.t these samples after convergence, and experimental results reveal the obvious better clustering quality of the proposed approach on average, compared to the state-of the-art ones.
End users in students’ participatory design process

This exploratory case study aims to shed light on how end users were considered in students’ design discussions and final design products. A 3-month participatory design project for students (ages 14–15) was designed, with the design brief: “co-design and make an e-textile product for the preschoolers according to their wishes and needs”. We analyzed transcribed end-users-related design discussions and the final products of two teams. The findings indicate that students’ end-users-related design discussions concerned various functional, technical, and visual/aesthetic features, as well as aspects beyond functional, such as students’ memories and experiences. Additionally, many concrete and abstract features and solutions of the final products were traced back to end users. This study suggests new possibilities for engaging students in empathic and reflective (digital) design and making, targeting design-literate citizens in the 21st century.
DEVELOPMENT OF MAGAZINE AS A LEARNING MEDIA ON ENDOCRINE SYSTEM

The magazine is one of the learning media that is rarely used in school. This research aimed to know the feasibility of endocrine system magazines and examine the learners' response to magazines as the media. This type of research is research and development. Five expert judgments tested the feasibility of the magazine. The aspects that were assessed were the material and media aspects. The product trials were done on nine students—the responses measured included cognitive, affective, and conative. The result of this research showed, on the feasible test CVR score of the material and media aspects was 1. Moreover, the CVI score was one as well. On the product trial, the score for cognitive aspect 85,18%, affective 83,33%, conative 87,77%, and the average responses 85,42%. It can be concluded that endocrine system magazine was feasible as learning media. Furthermore, magazines as a media gained a positive and strong response. So magazines can be a choice of media that can help learning activities.
LibGuides: AUNY - Literacy: Reference

This library guide will help you find resources to support your studies in your pursuit of your Master's of Science in Education in Literacy through AUNY.
Protecting Fair Use from Algorithms, Internet Platforms and the Copyright Office: A Critique of the § 512 Study

One context in which algorithms play an increasingly important role is in the policing of copyright infringement online. Ironically, however, an artist who attempts today to make a fair use of a copyrighted work and to share it with the world will face greater obstacles than she would have twenty years ago, despite the major advances in technology that have occurred during that time. Algorithms will detect the copyrighted material and may prevent the work from being shared at all, may remove the work from the platform after it is uploaded, and may even preclude the creator’s access to the judicial system to litigate her claimed fair use.Against this significant backdrop, the U.S. Copyright Office released a Study in May 2020 entitled “Section 512 of Title 17: A Report of the Register of Copyrights.” The aim of the Study was to evaluate potential problems with and updates to the DMCA. But fair use is barely mentioned in the Study. Nor does the Study adequately address the dangers to fair use posed by some of the technological and legislative changes the Study contemplates—including use of algorithms, DMCA+ systems, and notice-and-staydown systems.This Note argues that the U.S. Copyright Office’s recent Study discussing potential changes to the DMCA statute should have called for the protection of the fair use doctrine from the rise of algorithmic technology by advocating the preservation of the current safe harbors and rejecting both DMCA+ and notice-and-staydown systems as potential Standard Technical Measures. Part I of this Note provides background information about the doctrine of fair use and the DMCA in general. Part II then explores algorithms and their use within DMCA systems and will distinguish between the traditional DMCA framework and DMCA+ systems. Part III discusses the recent Copyright Office Study and notes the areas in which it falls short. Finally, Part IV highlights the potential dangers of implementing strict platform liability, DMCA+ systems, or notice-and-staydown systems and will instead advocate for legislative and judicial solutions that preserve the fair use doctrine and users’ freedoms of expression.
Implications of blockchain technology on corporate governance in India

With the advent of technology, humans have made it possible to arrive at a place where they can carry out huge transactions all by themselves without the help of any intermediaries. This technology is very famously called blockchain technology (BCT). The primary focus of this article is to see what implications BCT would have on corporate governance being specific to India. Corporate governance includes the government, big companies, and financial institutions that work through a framework for the smooth functioning of the companies. Although BCT is at a very primitive stage in India, if implemented it can fix the lop-sidedness of corporate governance in India for it provides greater transparency, efficiency, and security. The author through this article has explained the working of blockchain technology. Finally, the author has emphasized the need of adopting BCT for better corporate governance in India.
The Testing Database as Pandemic Technology: Reflections on the COVID-19 Response in India

This article examines the COVID-19 response in India, viewing it as deeply enmeshed in the dynamics of the ‘database’ as an emerging technology of governmentality. Databases aim to translate entire populations into units of information abstracted from social identities and local specificities. In the context of the coronavirus pandemic, bureaucratic state systems attempt to manage and respond to the health crisis via databases collating testing data across the country. Problematising COVID-19 testing databases, we delve into the logic of database governance. We find that as a tool of governance the database falters in its attempts to compress complex identities and locations into de-contextualised units of information. As the complexity of lived reality interrupts the logic of databasing, state discourse on ‘unintended consequences’, ‘leakages’, ‘duplication’, and ‘reconciliation’ processes in the management of databases abounds and the ambivalence of databases becomes manifest in the COVID-19 response. In this article, we use secondary data to understand how testing databases intervene and interact with complex realities to establish bureaucratic order around a pandemic. We posit that COVID-19 testing databases should be understood as being embedded in emerging database governmentalities that supplant care of the population with the maintenance of databases.
SCHOOL PLANT AND ACADEMIC PERFORMANCE OF STUDENTS IN PUBLIC SECONDARY SCHOOLS IN NNEWI EDUCATION ZONE OF ANAMBRA STATE: doi>10.24250/jpe/ no2/2021/ECA/COA

This study was aimed at determining the relationship betweenschool plant and student academic performance in publicsecondary schools in Nnewi Education zone of AnambraState. Four Research questions and four hypotheses guidedthe study. Correlation research design was adopted. Thepopulation of the study was 1779 respondents made up of 49Principals and 1730 students in the 49 schools in NnewiEducation zone of Anambra state. The sample comprised of88 respondents selected through simple random samplingtechnique. For data collection, questionnaires weredistributed to students and principals. The instrument wasvalidated by experts and reliability ascertained usingCronbach Alpha. The reliability index of 0.82 was deemedhigh enough. Mean score was used to answer the fourresearch questions. The chi-square test was used in testinghypotheses one while hypothesis two, three and four weretested using the t-test statistic. From the results, it wasdiscovered among others that there is a positive relationshipbetween school plant and students’ academic performance;that schools in urban areas have more components of schoolplants than their counterparts in the rural areas; principals inthe urban areas had positive attitude towards school plantmaintenance while the principals in rural areas had negativeattitude towards school plant maintenance. Based on thefindings, recommendations were made including a clarioncall on the state government to increase funding to schoolsin the annual budget, and make efforts to balance the allocationof the education sector equally among schools in the urbanareas and those in the rural areas. Conclusions were drawnand implications and suggestions for further studies made.
Comparison of Naive Bayes and Random Forests Classifier in the Classification of News Article Popularity as Learning Material

Tweentribune.com is a website that provides news texts with Lexile level information for each text.The Lexile level information feature is useful as a consideration for visitors to choose text that has a level of difficulty that matches their age.With such information, any news text on the site can be used as attractive teaching material for both teachers and students.However, the number of visits to each text varies widely.This study assumes that the popularity of each text is influenced not only by Lexile level information but also by other text characteristics.This research produces a number of engineering features that are extracted from the text to be used as a predictive attribute in classifying the popularity of the texts in question.Naïve Bayes and Random Forest are two classifiers used together with two popularity cluster scenarios based on k-means clustering.The results of the testing and evaluation phase show that the Random Forest algorithm has the best performance, with an accuracy value of 99.75%, an average recall value of 99.7%, and an average precision value of 98.7%.
Machine Learning Infrastructure on the Frontier of Virtual Unwrapping

Virtual unwrapping is a software pipeline for the noninvasive recovery of texts inside damaged manuscripts via the analysis of three dimensional tomographic data, typically X-ray micro-CT. Recent advancements to the virtual unwrapping pipeline include the use of trained models to perform the “texturing” phase, where the content written upon a surface is extracted from the 3D volume and projected onto a surface mesh representing that page. Trained models are critical for their ability to discern subtle changes that indicate the presence or absence of writing at a given point on the surface. The unique datasets and computational pipeline required to train and make use of these models make it a challenge to develop succinct, reliable, and reproducible research infrastructure. This paper presents our response to that challenge and outlines our framework designed to support the ongoing development of machine learning models to advance the capability of virtual unwrapping. Our approach is designed on the principles of visualization, automation, data access, metadata, and consistent benchmarks.
Robust noise feature extraction for efficient classification

In current paper, authors try to investigate regarding robust noise parameter to be used for efficient noise classification in noise parameter analysis which will then be used for intelligent volume control. One hundred and twenty original noises from environment were recorded with the help of a microphone connected to personal computer and stored as a noise samples in the computer’s memory. We have used MATLAB software for the software implementation of noise parameter analysis. Built-in programs have been used for Linear predictive coding (LPC) and Real cepstral parameter (RCEP) whereas user defined program was written for Mel Frequency Cepstral coefficient (MFCC) to get variation in noise parameters which may be further used for analysing the noise samples and classified through any one of the soft computing techniques viz. neural networks, fuzzy logic, genetic algorithms or a combination of these.  Forty samples each of three commonly encountered environmental noises (car, market and train) i.e., 120 noises in total have been considered in our study for estimation of three coefficients viz. Mel Frequency Cepstral coefficient, Linear predictive coding and real cepstral parameter. Our experimental results show that Mel Frequency Cepstral Frequencies are robust features for finding out variation in noise parameter estimates.How to cite this article:Mishra BP, Dwivedi R, Singh U et al. Robust Noise Feature Extraction for Efficient Classification. J Adv Res Sig Proc Appl 2021; 3(2): 1-5.
Multi-layered architectures for autonomous systems

Deliberation is a key feature to endow advanced intelligence to autonomous agents. Complex use cases, specially robotic applications which require a considerable degree of autonomy, have to be coordinated by some kind of control architecture. However, reusing existing architectures can be difficult because use cases are very heterogeneous. The main goal of this thesis is to ease the use of standard deliberative techniques in use-case oriented cognitive architectures for autonomous systems. This thesis contributes with new declarative languages to model use cases, new control architectures, new software developments and new methodological guidelines of how to apply these concepts in engineering processes. To justify the contributions, this thesis presents the design of NAOTherapist, an autonomous social robot for upper-limb pediatric rehabilitation. Its control architecture is a prototype that maximizes its generalization capabilities. However, several limitations arose when reusing this architecture in other projects as the Clarc robot (geriatric assessment) and a CoBot robot (office delivery tasks). The discussion about NAOTherapist is used to highlight these limitations and determine how to overcome them. The main implementation after all these approaches is Mlaras (Multi-Layered ARchitecture for Autonomous Systems), which is a generic architecture to ease the development of autonomous intelligent systems. It also allows non-expert users to directly modify it to refine the use-case definition. Mlaras is focused on automated planning as the core of its deliberative processes, which are separated into several layers to take advantage of the hierarchical abstraction levels present in many use cases. An instance of Mlaras has been developed for the autonomous agents of a simulated logistics competition use case to further evaluate the capabilities of the new architecture. The experimentation concludes that the contributions help to fill the gap between the use case definitions and the actual developed applications.
From Molecules to Quantum Computers: A Research Retrospective

Quantum computers promise to unlock new capabilities in areas ranging from the study of physical systems to cryptography. The field of quantum computing is built on the solid foundations of computational science that the Department of Energy Computational Science Graduate Fellowship continues to build. In this special research retrospective, I discuss my path in quantum computing research that receiving this award made possible. Starting with an introduction to quantum computing, I discuss how variational methods came to be popular on quantum computing hardware today, and the need to mitigate errors. The setting of variational algorithms facilitates a connection between quantum computers and the theory of machine learning, leading to discovery of phenomena, such as barren plateaus in the training landscape. This connection has allowed us to better understand the role of data, and how data can elevate machine learning models above traditional computation.
Compression of medical images by quaternionic wavelet transform

Technological developments for several years have resulted in the handling (storing, exchanging or processing) of increasingly important data in various fields and particularly in medical field. In this works we present a new image compression / decompression algorithm based on the quaternion wavelet transform (QWT). This algorithm is simple, fast and efficient. It has been applied to medical images. The results obtained after decompression are appreciated through the compression parameter values of CR, PSNR, and MSE and by visual observation. By the values of these parameters, the results of the algorithm are considered encouraging.