Reviewerly/exHarmony huggingface dataset:
Hybrid Conference Experiences in the ARENA

We propose supporting hybrid conference experiences using the Augmented Reality Edge Network Architecture (ARENA). ARENA is a platform based on web technologies that simplifies the creation of collaborative mixed reality for standard Web Browsers (Chrome, Firefox) in VR, Headset AR/VR Browsers (Magic Leap, Hololens, Oculus Quest 2), and mobile AR (WebXR Viewer for iOS, Chrome with experimental flags for Android, and our own custom WebXR fork for iOS). We use a 3D scan of the conference venue as the backdrop environment for remote users and a model to stage various AR interactions for in-person users. Remote participants can use VR in a browser or a VR headset to navigate the scene. In-person participants can use AR headsets or mobile AR through WebXR browsers to see and hear remote users. ARENA can scale up to hundreds of users in the same scene and provides audio and video with spatial sound that can more closely capture real-world interactions.
An interface between natural language and abstract argumentation frameworks for real-time debate analysis

Participatory approaches are increasingly being used to plan policies, usually involving stakeholders with conflicting interests, visions and objectives. Participants are often passionate about their cause, especially in sensitive contexts such as their health or the environment for example. Whether involved in, or observing a debate, more often than not, it is hard to follow its progress; positions are unclear and arguments are unstructured often resulting in circular discussions. This is true both for participants in an ongoing debate hoping to reach a consensus as well as for those who, a posteriori, wish to understand what was discussed and how. However, at the current time, there are to our knowledge no tools to support real-time debates by allowing participants to visualize arguments and identifying opposing points of view in order to resolve conflicts. In order to fill this gap, we developed a model based on Dung's argumentation framework that we implemented in a tool called AIPA, along with a web-application to facilitate user interaction. AIPA allows one to formalize and visualize, in real-time, the arguments of the participants, to conduct inferences in order to identify acceptable arguments and to highlight conflicting ones. Furthermore, AIPA can be used a posteriori to summarize a debate in an easy to follow argument representation. The main contribution of AIPA is its ability to structure the debate by making the arguments chain traceable and transparent, in real-time and a posteriori, and this, with users with no expertise in argumentation. In this paper, we present AIPA along with two applications to illustrate its workings and benefits.
Quality of Higher Education: Operationalization of the Concept

The purpose of the study is to operationalize the concept of “quality of higher educationˮ in sociological discourse. The methodological basis of the research is represented by modern sociological theories, within the framework of which the understanding of the problems of higher education development is carried out. &nbsp; Research results. The social and scientific relevance of the study of the stated problem is determined, the signs of the quality of higher education are identified, its resources (social, material, technical and informational) are established, the separation from related concepts is carried out, and finally an instrumental definition that can be used in scientific polemics in the new emerging discourse initiated by the on-lineization of the social institute of higher school is given. It is concluded that in real educational practices, access to an understanding of the higher education quality is possible only through the coordination of the views of the main social actors of higher education institution.&nbsp; &nbsp; Prospects of the study are in further understanding and empirical verification of the quality problems of modern higher education.
Tampered Regions Detection for JPEG Images by Using DCT Coefficients of Re-Saved Image

Recently, digital images have been used in various situations. However, development of image editing software has made image editing easier, which has caused the problem of maliciously tampered images. Therefore, in order to ensure the reliability of digital images, it is necessary to detect whether it has been tampered with or not. On the other hand, the JPEG format is the most famous image file format. This is because this compression rate is higher than that of other file formats. In this study, we propose a novel method to detect tampered regions in a JPEG image using its DCT coefficient. The proposed method deteriorates an input image by re-saving it with another JPEG format. The tampered regions are detected by using the difference image obtained from an input image and its re-saved image in the DCT domain. The effectiveness of the proposed method was verified by experimental results.
Sample size for clustered count data based on discrete Weibull regression model

In this study we consider the problem of sample size determination for clustered count data based on clustered discrete Weibull regression model. Many papers that deal with sample size calculation for clustered count data have a strong assumption about the dispersion type of the data. Poisson, negative binomial are widely used models however their assumptions are hard to be met in real life. Here we propose to use discrete Weibull regression model which can handle both under and over dispersed type of data for clustered count data when calculating the sample size. We incorporate a random intercept to consider the correlation between the subjects in each cluster. We used the h-likelihood method and Monte Carlo simulation to estimate the required sample size for clustered count data. Extensive simulation study is also performed to examine the effect of the skewness of the data, variance of the random effect and the number of the clusters on calculating the required sample calculation.
Classification of Indian English Poetry into Pre-Independence and Post-Independence Eras using Combination of Semantics, Topics and Style features

Automatic classification of poetry era is a challenging task. In Indian English Poetry, the poems are categorized into two eras named Pre-Independence and Post-Independence. The poetry style and themes are changed from one era to another era depending on the authors era that he/she belongs to. Hence, this study is testing of different feature selection methods and ensembled features to identify the poems era automatically. The poetry classification can be carried out on semantics, topics and style features. In this experiment, we have used Latent Semantic Analysis (LSA) to find semantic features, Latent Dirichlet Allocation (LDA) topic modelingto find topic features, along with these phonemics, syntactic elements and structure of poetry as style features. The experiment is carried out on 760 poems written by 28 authors, in this, 344 belongs to Pre-Independence era and 416 belongs to Post-Independence era. The classification accuracy 91.20% is achieved using Random Forest classifier with combination of LSA and LDAfeature set. Further with the combination of style features to LSA and LDA features the classification result achieved is 92%. The study showed that the poetry can be classified into different eras with decent accuracy based on the combination of topics, wordsand style of poetry as features.
Web-Based Knowledge Management Distributed Application for Medical Certificates Using Blockchain Technology

Globally, disruptive technologies play a vital role in ordinary people’s lives to technocrats, small-scale industry to multinational companies, and private sectors to government sectors. Providing security and privacy preservation of the sensitive data in various applications related to a bank, healthcare sectors, etc., are becoming significant issues with advanced technologies. Unauthorized persons and also attackers get benefits from the features. Permissionless and open access features will become advantages to the cyber attackers to crack the data. These issues will be resolved by adopting some knowledge management systems (KMSs) like blockchain technology, and Artificial Intelligence. Therefore, this chapter proposed a web-based knowledge management decentralized application using blockchain technology in healthcare sectors. Especially in the healthcare centers, people’s sensitive personal data such as name, address, financial details, and cause maintain as records. Cyber attackers treat the medical records as treasure. Advanced featured technology, blockchain incorporation with web-based applications can reduce the attacks like ransomware, phishing, Brute force, steals credentials, and Man in the middle attack. The technology’s main inherited features are distributed, immutable, transparency, security, and peer-to-peer network communication. This hybrid approach, a combination of Web 3.0 and blockchain technology, increases the privacy and preservation of official medical documents issued by healthcare centers. This chapter illustrated the process of a proposed decentralized application using Ethereum-based Blockchain with Metamask wallet. This work’s main strength is the proposed application deployed results over an ethereum blockchain platform on test RPC, which are laps in the maximum related literature survey works currently available. Furthermore, presented comprehensively about the deployment cost of the application operations in terms of smart contracts and execution time for uploading credential requests.
Text Summarization using Ml and Nlp

Quantity of data produced per day is around 2.5 quintillion bytes. Right now, no one has the time to pursue each and everything. With the growth of technology and digital media, people are becoming very lazy; they are looking for everything more smartly. If they want to read any article or newspaper, they cannot go through every line that has been given. To overcome this problem, an automatic text summarizer using Machine Learning (ML) and Natural Language Processing (NLP) with the python programming language has been introduced. This automatic text summarizer will generate a concise and meaningful summary of the text from resources like textbooks, articles, messages by using a text ranking algorithm. The input text that is given will be split into sentences; these sentences are again converted into vectors. These vectors are represented as a similarity matrix and based on these similarities; matrices sentence rankings will be given. The higher ranked sentences will be the final summary of the given input text
Waste Bank Data Management Information System with Web-Based Telegram Bot Notification Feature at Mawar Garbage Bank

The Mawar Garbage Bank needs a waste bank data management information system with a Web-based Telegram Bot notification feature on the Garbage Bank. The goal is to provide convenience to the officers of the Mawar Garbage Bank to speed up the process of managing data and delivering information in an effective and efficient manner to customers. This research uses the waterfall method. By using a MySQL database, PHP programming language and Laravel Framework. As well as diagrams used for system modeling using the Unified Modeling Language (UML). With this system, it is hoped that it can make it easier to manage data and provide information about the waste bank which is useful for the Mawar Garbage Bank. This research will be tested using Black Box, White Box and User Acceptance Test (UAT). As for the notification using a telegram bot. The notification from the telegram bot aims to make it easier for customers to find information such as balances, savings flows, and cash withdrawals. From the black box test, you get 100% results, which means that the system has met the design objectives, so this system is suitable for use. White box test results get 100% results with the resulting complexity is 2 which shows that it can meet the criteria for software engineering. The results of the user acceptance test (UAT) test get a 98% result, which means that the software is acceptable and has met the requested needs.
Lache, Giere, Boeie

The present paper focuses on the use of insubordinate infinitives in social media writing produced by Flemish adolescents. It combines insights from usage-based, cross-linguistic research on insubordination with expertise in the sociolinguistics of online communication. We demonstrate how insubordinate infinitives, in view of their evaluative and discursive functions, find a perfect breeding ground in informal online communication and how this particular context pushes the insubordinate infinitives into notably advanced evolutionary stages.
Motivation to Participate in Professional Development in Technologically Intensive Work Environments

Maintaining professional competency in an environment with rapid technological innovation may seem to be an insurmountable task since new technologies often become obsolete before technology professionals can master them. Although research has established that challenging work assignments affect professional motivation, research has also established that overly challenging work assignments can demotivate people. In particular, research has called for work that examines the relationship between the technical updating climate (TUC) and learning motivation in the professional development activity context. To fill this gap, we collected data from 174 IT professionals who exemplify professionals working in such environments. We found evidence that showed locus of control, self-efficacy, and technical updating climate could predict 43 percent of the variation in the IT professional’s motivation to participate in professional development. The research model that we propose demonstrates a strong ability to explain motivation to engage in professional development in technologically intensive work contexts. Furthermore, we successfully operationalized and validated the technical updating climate as both an instance of positive climate and as an organizational climate.
Chapter 4. Semantic textual similarity based on deep learning

This study proposes an original methodology to underpin the operation of new generation Translation Memory (TM) systems where the translations to be retrieved from the TM database are matched not on the basis of Levenshtein (edit) distance but by employing innovative Natural Language Processing (NLP) and Deep Learning (DL) techniques. Three DL sentence encoders were experimented with to retrieve TM matches in English-Spanish sentence pairs from the DGT TM dataset. Each sentence encoder was compared with Okapi which uses edit distance to retrieve the best match.1 The automatic evaluation shows the benefit of the DL technology for TM matching and holds promise for the implementation of the TM tool itself, which is our next project.
Prediction of Cervical Cancer Using Machine Learning

Cancer remains one of the most prevalent diseases humans are confronting. Even after taking huge strides in medicine, humans have not been able to properly treat cancer. Cancer of the cervix is counted among the deadliest diseases faced by women of the world. It grows in the lower lining of the cervix. Cervical cancer is the fourth most prevalent form of cancer in women around the globe; it propagates in 6% of patients who are diagnosed with cancer around the world. Less than half of the patients screened get flagged at an early stage. The low success in finding cancer may be due to the lack of awareness and vigilance of the disease. Deficiency of appropriate medical apparatus and infrastructure further deteriorates the problem, and this can lead to huge complications. In a huge country like India, the five-year survival rate goes as low as 50%. Even if the cancer is eradicated efficaciously, a minority of cancer cells can remain hidden. This can lead to the recurrence of cancer. Patients showing symptoms of recurrence have a low survival rate, as low as 22%. As patients show diverse growth dynamics, researchers are trying to find the root cause of the recurrence of cancer. They are trying to find neoteric and innovative ways to identify the origin of recurrence before cancer once again wreaks havoc in people’s lives. Researchers are now looking to facilitate their research by using high-level machine learning/artificial intelligence models. Organizations like BioGPS and University of California, Irvine (UCI) machine learning repository are keeping huge datasets, which can be used for research and training models. Researchers are working with SVM, C5.0, J48 graft, artificial neural network, Naive Bayes, and extreme learning machine to assist physicians with flagging, finding, and pointing out problems and discrepancies of cancer at early stages. By devising this methodology, we can find out which approach is the most effective and accurate way of helping future female patients through early detection and decreasing the chance of relapse.
Learner Need Satisfaction and Effort in an Online English Language Learning Context

This study aims to explore learners’ need satisfaction and effort in an online English language learning setting, focusing on Korean college students. A total of seventy-seven participants were divided into low-, intermediate-, and high-proficiency groups based on their English competence. Four major instruments were employed in the current study: a background questionnaire, an assessment of English competency, the Need Satisfaction in an Online English Learning (NSOEL) questionnaire, and the Online English Learning Effort (OELE) questionnaire. The results indicate that learners with high- and intermediate-levels showed significantly greater performance than those in the low-proficiency group in terms of need satisfaction for competence on the NSOEL and also substantive effort on the OELE. This study also reveals that there existed positive correlations among learners’ proficiency levels, factors on the NSOEL, and factors on the OELE. Based on the findings of the study, pedagogical implications and practical teaching methodologies are given for online English language learning instruction to help foster L2 learners’ need satisfaction and effort.
Utterance Position-Aware Dialogue Act Recognition

This study proposes an utterance positionaware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance's absolute or relative position.The proposed approach is inspired by the observation that some dialogue acts have tendencies of occurrence positions.The evaluations on the Switchboard corpus show that the proposed positional encoding of utterances statistically significantly improves the performance of DAR.
Sentiment analysis for covid-19 in Indonesia on Twitter with TF-IDF featured extraction and stochastic gradient descent

Twitter is an information platform that can be used by any internet user. The opinions of the Twitter Netizens are still random or unclassified. The technique for classifying sentiment analysis requires an algorithm. One of the classification algorithms is Stochastic Gradient Descent (SGD). The more training data provided to the machine, the accuracy of the classification function model formed by the machine is also higher. But in making representations into numerical vectors, the dimensions of data become large due to the many features. Feature optimization needs to be done to the training data by reducing the dimensions of the training data while maintaining high model accuracy. The optimization feature used is the TF-IDF (term frequency-inverse document frequency) feature extraction. sentiment analysis using TF-IDF feature extraction and stochastic gradient descent algorithm can classify Indonesian text appropriately according to positive and negative sentiment. Classification Performance using TF-IDF feature extraction and stochastic gradient descent algorithm obtained an accuracy is 85.141%.
Design of thin‐structured‐light projection system for small‐height measurement

A thin-structured light projection system with simple setup is proposed and applied to small-height measurement. In this system, the design of structured light pattern is carried out on the computer. The structure, color, geometric dimension, motion mode, projection brightness, contrast, and other attributes of the pattern can be edited easily. The optical system with zoom function is designed, which can output thin-structured-light fringe with a minimum width of 10 μm. The camera and microscope are used to construct a vision system, which is used to capture the pattern of structured light. The three-dimensional (3D) shape reconstruction is realized by analyzing the pattern of structured light. The results show that this system can project a small width of thin-structured light pattern, which is very suitable for 3D shape reconstruction of microscopic objects with the small-height of tens of microns to hundreds of microns.
Intra- and Inter-textual Syntactic Priming in Original and Translated English

This article presents and discusses the results from a corpus-based study on the variation between English complement clauses with and without that in original versus translated English.The study is primarily aimed at disentangling the effects of, on the one hand, intratextual structural priming, i.e. the influence from a relevantly similar construction produced earlier in the (target) text, and, on the other hand, intertextual priming from the source text to the target text, i.e. the influence from a relatively similar source language construction encountered in the to be translated stretch of source text.The results indicate that whereas regular intratextual priming has the expected effect in the original English texts, this effect disappears in the translated texts, where intertextual priming turns out to be the most relevant mechanism.
Custom Alexa Skills

Now that you’ve published your first Alexa skill, let’s go back through the steps and look at each of them a little more in depth. In Part I, you created an Alexa skill from VUI development to back-end code to publishing in the Alexa Skills Store. In Part II, we’ll develop another skill, and we’ll see how to use some of the more advanced features of the Alexa platform.
White Blood Cell Detection and Segmentation Combined with the Channel Space Weighted Feature Pyramid Networks

<p indent=0mm>The accurate detection and segmentation of leukocytes is a challenging task in medical image processing. The white blood cell image obtained under microscope is easily affected by impurities. There are many kinds of white cells, different shapes and small differences among them, and also exiting overlapping and adhesion phenomena, which leads to the inaccuracy of cell edge segmentation. The above problems are always the difficulties of detection and segmentation of white cell image. To solve the above problems, a leukocyte detection method based on Mask R-CNN and attention mechanism multi-scale feature fusion is proposed. Based on the Mask R-CNN structure, proposed thesis integrates the attention mechanism module in the FPN (feature pyramid networks) module, and proposes the CSFPN (channel spatial feature pyramid networks) structure. This structure can learn the weight of important channel features and the representation of important feature regions in the feature maps. At the same time, Skip-FPN module is added to the network structure, which fuses more low-level detailed information of leukocytes through short connection, so as to detect and segment leukocytes more accurately. Experimental results show that this method has good detection and segmentation performance. Under the Kaggle open source data set, the mAP value of this method for white blood cell detection reaches 98.25%, which has an increase of 1.25% compared with the previous improvement. The accuracy mIoU value reached 89.3%, which has an increase of 0.002% compared to the before improvement.