Reviewerly/exHarmony huggingface dataset:
Web Application for managing the Techno-pedagogical Assessment of Virtual Learning Environments in the Virtual Campus of the Central University of Venezuela: GENETVI

This research was contextualized in the Distance Education System of the UCV where the absence of a comprehensive evaluation mechanism from the perspective of the students and of the teaching coordination of the Virtual Learning Environments was evidenced, at the undergraduate and graduate levels and/or extension either in distance, mixed mode or as support to face-to-face subjects, hosted in the Virtual Campus. Reason for which, to systematize and automate this process, assessment instruments based on the MEI-SEaD Model were built, which were implemented in a Web Application called GENETVI, incorporated into the University's Virtual Campus, as an innovative tool for the management of the evaluation from a pedagogical and technological vision of these educational spaces for their constant improvement. For the construction of this tool, an adaptation of the Scrum framework was used, including the use of the following technologies: on the server side there is the Laravel framework with the use of its object-relational mapping technique (ORM ) that is compatible with various database management systems such as PostgreSQL or Mysql, while on the client side, it has the hypertext markup language (HTML 5), cascading style sheets (CSS) and the JQuery libraries and Bootstrap.
Highly Training Algorithm for Enhancement of Speech Signal Data (HTA-ESSD)

The enhancement of speech aims to maximize the quality of speech by utilizing HTA (Highly Training Algorithm).The main aim of enhancement is to maximize the intelligibility or perceptual quality of the speech signal data.We represent HTA, aimed at fast removal and very effective of background noise from the signal-channel of speech signal data based on analytically determined output-weights and randomly selected-hidden units.The feature learning with HTA may not be effective for the natural signals, even with the larger number of the hidden nodes, C-HTA (Classified Highly Training Algorithm) are employed by leveraging the sparse auto-encoders.This work is mainly to apply C-HTA and HTA to enhance the speech-signal data.The proposed HTA is evaluated on Aurora database at three SNRs.We also compare our introduced algorithm with many state-artmethods.
Excerpt from The Official Anti-Milo Toolkit

Abstract The organizers of the Anti-Milo Toolkit aimed to contribute to a broader counter-movement that would make it easier for university campuses to challenge and de-platform white supremacist and fascist speakers sheltered under the auspices of “free speech.” This toolkit gathers info-tracts, syllabi, flyer templates, and other activist materials collected and widely distributed across campuses in preparation to protest Breitbart journalist Milo Yiannopoulos's 2016 “Dangerous Faggot Tour.” Yiannopoulos was slated to speak at thirteen college campuses in support of an alt-right platform founded upon the weaponization of “free speech,” xenophobia, and transphobia. In addition to providing materials for protest, the authors of the “Anti-Milo Toolkit” take critical aim at appropriation of liberal-academic vocabularies by right-wing groups and Yiannopoulos's history of outing trans and undocumented students at his events, and call for widespread action against the spread of violent rhetoric targeting marginalized communities in order to maintain the university as a space of sanctuary. Because of its accessibility via digital channels and its wide range of short and readable pieces written in a variety of styles, and because of the collective's wide network of organizing connections, the kit circulated very broadly.
An Improved Approach for Fast Documents Scrapping and Classifying Using Selenium Automation and Multinomial Naïve Bayes Classifier

Generally, Selenium Automation is used for testing purpose and detecting errors and defects of the system in development. However, we will use Selenium for making a list of required web element from web page. In addition, by using that list selenium will identify the new documents from web pages for scrapping data. For example, we consider any tenders site, there may be thousands of tenders getting published every day, so it is very hard for the user to surf every tender one after another to get the tender of his/her need. But, in our method firstly, we are using bag of words method to gather test data for further classification. Secondly, we are using Multinomial Naive Bayes Classifier to classify our documents industry wise which will be useful for the user to pick up his category fresh tender. For picking up a fresh tender, user will access the folder created on the desktop where the scraped fresh documents will be stored in a technology wise folder. In the last, Confusion Matrix will be built and detailed accuracy by class for the technology category will be calculated and shown. This approach helps the larger service providing business organizations to provide their clients the documents of their needed categories.
Modeling the Severity of Complaints in Social Media

The speech act of complaining is used by humans to communicate a negative mismatch between reality and expectations as a reaction to an unfavorable situation. Linguistic theory of pragmatics categorizes complaints into various severity levels based on the face-threat that the complainer is willing to undertake. This is particularly useful for understanding the intent of complainers and how humans develop suitable apology strategies. In this paper, we study the severity level of complaints for the first time in computational linguistics. To facilitate this, we enrich a publicly available data set of complaints with four severity categories and train different transformer-based networks combined with linguistic information achieving 55.7 macro F1. We also jointly model binary complaint classification and complaint severity in a multi-task setting achieving new state-of-the-art results on binary complaint detection reaching up to 88.2 macro F1. Finally, we present a qualitative analysis of the behavior of our models in predicting complaint severity levels.
The Role of Case Study Method in Shaping Analytical Skills of an Editor

The article considers the experience of using case study method in the sphere of journalism education. This method is focused on building analytical skills of future editors. The author points out that case study method helps to adopt professional courses to the information market demands, updates the students’ need for searching development trajectories within their specializations.
Dados abertos e ciência cidadã em instituições de memória

The open science movement, based on the statements of Budapest (2002), Bethesda (2003) and Berlin (2003), and in line with the Sustainable Development Goals, defined by the UN in 2015, aims at accessing scientific literature through free availability and widespread scientific data on the WWW. In memory institutions, the availability of open access content aims to connect with users, in a new form of relationship benefited by the use of Web 2.0 platforms and citizen science projects. The purpose of this article is to identify and map trends in open data and citizen science projects in the National Archives, Library and Museum and to verify whether they are (or not) aligned with their foreign counterparts. It concludes by the still incipient but growing trend in Portugal, which can be partially explained by the fact that the coordination services in these areas do not promote projects on the Web 2.0.
CONTENT OF DIGITAL EDUCATIONAL RESOURCES AND STUDENT INVOLVEMENT IN THE PROCESS OF DISTANCE LEARNING

T. Bukina 1, L. Malakhova 2, E. Novgorodova 2, E. Nelyubina 3 1Kazan Federal University (RUSSIAN FEDERATION) 2Kazan State Institute of Culture (RUSSIAN FEDERATION) 3Kazan Branch of The Russian State University of Justice (RUSSIAN FEDERATION)
The Evaluation System for the Balance of Information Ecosystem of College Network Education

This paper reviews the research status of college network education and the balance of information ecosystem. Based on analytic hierarchy process, the paper constructs the evaluation system for the balance of information ecosystem of college network education, and elaborates from five aspects: the network education information platform, teachers, students, the interaction between teachers and students, teaching and learning equipment. The paper determines evaluation index weights, and establishes the fuzzy evaluation set of the evaluation system for the balance of information ecosystem of college network education by using the fuzzy comprehensive evaluation method. It concludes that the balance of information ecosystem of college network education is in the middle level. On the basis of the evaluation results, the paper puts forward the corresponding optimization measures, in order to maintain the balance of the college network education industry.
Application of deep reinforcement learning and nursing theory to patients nursing assistant system

To reduce fall risks for the patients in medical and nursing care facilities, I have proposed a nursing assistant system applying artificial intelligence and robot technologies that predict incidents and warn people by automatically and realtime risk-assessing the condition of patients and equipment. However, it is very difficult to make judgments and actions immediately even if multi-professional cooperation or expert-level recognition can be done depending on the situation for medical staff. Therefore, in this paper, we introduce a case that shows recommended intervention methods by applying deep reinforcement learning and conceptual models of nursing theory to recognition / judgment / control model installed in autonomous mobile assistive robots.
Asymmetric CNN for image super-resolution

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. According to nature of different applications, designing appropriate CNN architectures is developed. However, customized architectures gather different features via treating all pixel points as equal to improve the performance of given application, which ignores the effects of local power pixel points and results in low training efficiency. In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. The AB utilizes one-dimensional asymmetric convolutions to intensify the square convolution kernels in horizontal and vertical directions for promoting the influences of local salient features for SISR. The MEB fuses all hierarchical low-frequency features from the AB via residual learning (RL) technique to resolve the long-term dependency problem and transforms obtained low-frequency features into high-frequency features. The HFFEB exploits low- and high-frequency features to obtain more robust super-resolution features and address excessive feature enhancement problem. Addditionally, it also takes charge of reconstructing a high-resolution (HR) image. Extensive experiments show that our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems. The code of the ACNet is shown at https://github.com/hellloxiaotian/ACNet.
China and the Future of AI

The progress of technology is currently remarkable and very fast, especially Artificial Intelligent or AI. China, as the largest emerging and biggest developing country, has significant AI growth. It is near that China will probably become the pioneer in the fifth generation of technologies, making the US worry. Along with its remarkable economic growth, China makes research and development agenda as the top priority. In this regard, this paper seeks to explain China and the future of AI, including its current progress.
Measurement-device-independent quantum key distribution based on GHZ-entangled-state

Quantum key distribution is the core technology of quantum communication. In 2012, the Measurement-Device-independent quantum key distribution protocol proposed by Lo et al., referred to as MDI-QKD protocol, can effectively resist detector attacks from eavesdroppers. However, the traditional MDI-QKD only supports two parties, which can no longer meet the actual needs in the era of rapid development of communication technology. Therefore, multi-party quantum key distribution has become one of the current hot spots. This paper proposes a multi-user measurement device-independent QKD method based on the GHZ entanglement state. The GHZ entanglement source is used as the quantum relay, which solves the multi-party communication and greatly improves the safe communication distance.
The Language of Internet Media: Linguocultural Aspect

The purpose of the article is to highlight the specific features of the language of Internet media.Research methodology. The analysis of the language of Internet publications has led to the use of general scientific methods, in particular, unstructured systematic observation, a descriptive technique used to specify the features in the language and style of online media.Results. Considering the active influence of the language of the Internet media on the formation of the culture of the Ukrainian language, it is necessary to introduce control over the quality of Internet journalism. To become the Internet more “literate”, the first step must be taken by the Internet media. The texts should be complied with the language norms. Nowadays online journalism is already trying to reach a new professional level. Opportunities inherent in the Internet (multimedia, interactivity, hypertextuality, etc.) allow us to modernize the old genre palette of journalism, to make it more effective (in relation to the features of the audience’s use of information on the web).Scientific novelty lies in clarifying linguistic trends in the Internet media, explaining the reasons for language shifts.The practical significance of the study is that the presented results can be used in further scientific and educational work, for example, in the development of lectures on the courses “New communication technologies and media”, “Typology of Internet media”, in the preparation of educational literature.
A family of projective two-weight linear codes

Projective two-weight linear codes are closely related to finite projective spaces and strongly regular graphs. In this paper, a family of $q$-ary projective two-weight linear codes is presented, where $q$ is a power of 2. The parameters of both the codes and their duals are excellent. As applications, the codes are used to derive strongly regular graphs with new parameters and secret sharing schemes with interesting access structures.
Complete training and update documents to “as commissioned”

This short chapter reviews the updating of training materials.
ISLA / IFLA course design: Principles and practical proposals for beginners' courses

This paper is a revised version of the one that appeared with many formal defects in the original publication.
Establishing security of Bitcoins using elliptic-curve cryptography-based protocol

It is undeniable that the growth of the computer and network security is repeatedly increased since the past 40 years. In 1985-1986 encryption was applied as a primary technique for providing security. Network security technology includes authentication and authorisation, data encryption, access control and security auditing. Bitcoin is presently one of the most popular crypto-currencies. The phenomenon of Bitcoin crypto-currencies is based on the public key cryptography and peer to peer network. In the Bitcoin, blockchain is used for storage of the transaction which is also called the public ledger. Digital money or Bitcoin is used in day to day activities, some of which are illicit and illegal. In this paper, the main area is security, i.e., the main focus here is to protect Bitcoin systems from unauthorised access and from various threats using an appropriate scheme (elliptic-curve cryptography, ECC) protocol.
On the Complexity of Learning Description Logic Ontologies

Ontologies are a popular way of representing domain knowledge, in particular, knowledge in domains related to life sciences. (Semi-)automating the process of building an ontology has attracted researchers from different communities into a field called "Ontology Learning". We provide a formal specification of the exact and the probably approximately correct learning models from computational learning theory. Then, we recall from the literature complexity results for learning lightweight description logic (DL) ontologies in these models. Finally, we highlight other approaches proposed in the literature for learning DL ontologies.
Back Diagonal Reflex Algorithm for Finding the Maximal Visual Area Polygon

A gradient search algorithm is proffered for finding the maximal visible area polygon (VAP) from &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;an interior point of a simple polygon . The area function over all points &nbsp;is not well &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;behaved, being in general neither convex nor concave, including points that may be non-differentiable and even discontinuous. The area function &nbsp;returns the visible area viewed by any point &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;in the polygon . We develop a "back diagonal partition" (BDP) of based on the vision properties of its reflex vertices. Our maximal VAP algorithm is gradient based and converges in a finite number of steps, It has polynomial complexity , for a polygon &nbsp;with vertices, and reflex vertices. The maximal VAP algorithm is embedded in a heuristic for solving the well-known guardhouse problem which is polynomial with time complexity of . Our main contributions are: (i) A back diagonal reflex (BDR) algorithm which is a noncontinuous gradient VAP algorithm over a &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;BDP domain, (ii) incorporation of this gradient algorithm into a probabilistic heuristic &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;algorithm for finding a point in &nbsp;which finds an approximate solution to the Max VAP problem, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and (iii) an efficient heuristic algorithm for the guardhouse problem based on the gradient VAP algorithm.
DEVELOPMENT AND VALIDATION OF AN INSTRUMENT TO MEASURE SOCIAL CONSTRUCTIVISM IN A VIRTUAL CLASSROOM

Virtual learning has never been an option for secondary education, but the pandemic situations have blurred this line. A considerable population of school students has been attending school from home for roughly five months now. Social Constructivist pedagogy is the need of the hour and, multiple research focussed on effective teaching and learning have endorsed it. Presently, there are no validated Instruments that, can measure and monitor the Social Constructivist domain, in a synchronous virtual classroom. This study discusses an Instrument developed to measure the social constructivist dimension of a virtual classroom in light of the concepts of Social Constructivism and Cybergogy. This paper includes an assessment of the pilot study. The evidence for the validity (Cronbach's alpha) and construct reliability of this Instrument assure that the presented assessment tool is well suited to measure the Social Constructivist dimension of a secondary level virtual classroom.
Updating Data on the Web

In this chapter, I will cover how to update data using Entity Framework Core 5 and Razor Pages in ASP.NET Core. First, I will go over the generated Edit Razor Page and model that were previously created. After that, I will cover how to update the Edit Razor Page to edit an address record for our edited person.
Data-free mixed-precision quantization using novel sensitivity metric.

Post-training quantization is a representative technique for compressing neural networks, making them smaller and more efficient for deployment on edge devices. However, an inaccessible user dataset often makes it difficult to ensure the quality of the quantized neural network in practice. In addition, existing approaches may use a single uniform bit-width across the network, resulting in significant accuracy degradation at extremely low bit-widths. To utilize multiple bit-width, sensitivity metric plays a key role in balancing accuracy and compression. In this paper, we propose a novel sensitivity metric that considers the effect of quantization error on task loss and interaction with other layers. Moreover, we develop labeled data generation methods that are not dependent on a specific operation of the neural network. Our experiments show that the proposed metric better represents quantization sensitivity, and generated data are more feasible to be applied to mixed-precision quantization.
Three-dimensional unwrapping algorithm based on reliability sorting following a path with added threshold

The phase unwrapping algorithm based on sorting by reliability is used to process the digital hologram interferogram, which is easy to be affected by low reliability pixels, resulting in the distortion of the three-dimensional surface. In view of the above problems, the algorithm based on sorting by reliability is improved, and the appropriate threshold is defined, so the unwrapping path is optimized. The simulation results show that the optimized unwrapping algorithm can effectively unwrap the three-dimensional surface morphology of the target, and the results are smooth and without distortion.
KLASIFIKASI BERITA HOAX PADA BERITA BERBAHASA INDONESIA MENGGUNAKAN LATENT DIRICHLET ALLOCATION DAN VECTOR SPACE MODEL

Hoax news is an argument that does not make sense (far-fetched) but made as if the news happened. The number of hoax news that has appeared on online news sites is one form of misuse of technological developments which makes it difficult to know the tendency of hoax news or valid for ordinary people. So the hoax news classification application is needed. Latent Dirichlet Allocation is one method of text mining that is used to summarize a document to get a discussion group of topics contained in the document or can be referred to as topic modeling. In this case, the Latent Dirichlet Allocation will be used for modeling topics in the news content dataset. The results of the topic modeling will be matched with the similarity function using the Vector Space Model to calculate the weighted of each term contained in all the results of the topics formed and the news title dataset is then classified based on the existing class. Before carrying out calculations, the text first goes through the process of tokenizing, filtering, and stemming. Based on the test results, we obtained an accuracy of 63.3%.