Reviewerly/exHarmony huggingface dataset:
Boccaccio, il Decameron e la Crusca: le fonti spogliate dagli Accademici

This survey concerns the lexicographic presence of Giovanni Boccaccio’s Decameron in the Vocabolario of the Accademici della Crusca (from the first to the fourth edition). Boccaccio is the author that counts 15.600 occurrences (considering also his others literary works) already in the first edition. This is a significant situation about Decameron, because we know that a lot of its passages went through modifications and censorship: all of these interventions on Boccaccio’s text would have had some influence on the quotations into the Vocabolario. Starting from the declarations of the Accademici, the study wants to investigate about the employment of Decameron’s sources during the works for the Vocabolario, involving editions, manuscripts and Accademici’s autograph papers.
Lexicon Based Sentiment Data Creation Technique for Product Protection (SDTCPP)

The market is pounded with the same hazardous material to users, specifically skin and healthcare.It also affects people both in terms of their health and economy.This makes it necessary to monitor products from user opinions for its protection from duplicates.Online users have increased rapidly and share their opinions on occasions, brands, individuals, products, and events occurring across the world.Millions of users participate in SNSs (Social Networking Sites) like Facebook, Twitters, Instagram and WhatsApp etc. Product reviews in public forums like SNSs can provide early clues on such duplicates in terms of allergies or adversities while using them.SA (Sentiment Analysis) can play a significant role in such surveillances.This paper proposes a framework for reviewing experiences in cosmetics in skincare using the Amazon reviews dataset.The proposed technique is based on safety lexicons which are trained for classifications from user sentiments.1. Introduction The global scourge of counterfeit drug and cosmetic products poses a significant threat to public safety.One strategy for combating counterfeit products is through the effective communication and tracking of early warning signals of product allergies, side or adverse effects, drug resistance and disease outbreaks [1].E-commerce websites have gained popularity in providing the ability to shop from home and at discounted prices.Online users, not the only shop, but also share their opinions on SNSs.Millions of comments or reviews are generated on these sites, making it complicated for an organization to trace and track public opinion continually.This makes it imperative to study and classify these reviews to extract useful information from this data centre [2].DM (Data Mining) techniques find their use of simplifying and extracting patterns from public sentiments.Thus, analyzing customer opinions is useful for business and has widened its scope in health care, sports, politics, recommendation system, and many other domains.Figure 1 depicts the SA Application Areas.
Retrospective motion correction for Fast Spin Echo based on conditional GAN with entropy loss

Abstract We proposed a new end-to-end motion correction method based on conditional generative adversarial network (GAN) and minimum entropy of MRI images for Fast Spin Echo (FSE) sequence. The network contains an encoder-decoder generator to generate the motion-corrected images and a PatchGAN discriminator to classify an image as either real (motion-free) or fake(motion-corrected). Moreover, the image’s entropy is set as one loss item in the cGAN’s loss as the entropy increases monotonically with the motion amplitude, indicating that entropy is a good criterion for motion. The results show that the proposed method can effectively reduce the artifacts and obtain high-quality motion-corrected images from the motion-affected images in both pre-clinical and clinical datasets.
The 2020 VGTC Virtual Reality Best Dissertation Award

The 2020 VGTC Virtual Reality Best Dissertation Award goes to Misha Sra, a 2018 graduate from the Massachusetts Institute of Technology, for her dissertation entitled, “A Framework for Enhancing the Sense of Presence in Virtual and Mixed Reality”.
professionals@DravidianLangTech-EACL2021: Malayalam Offensive Language Identification - A Minimalistic Approach

The submission is being made as a working note as part of the Offensive Language Identification in Dravidian Languages shared task. The proposed model “DrOLIC” uses IndicBERT and a simple 4-layered MLP to do the multiclass classification problem and we achieved an F1 score of 0.85 on the Malayalam dataset.
Enhancing Reading Skill via ReadTheory.org: Students’ Attitudes, Motivation, Autonomy and Perceptions

Studies into the use of gamification, “the use of game design elements in non-game contexts” (Deterding et al, 2011, p9) for language learning, has found positive results on language development and enhancing learner engagement toward the learning process, but they have mostly focused on the effectiveness of gamification rather on learner’s attitudes, motivation, and autonomy toward the gamified language learning experience. This research presents the results for a study into the use of a gamified reading website, Readtheory.org, that incorporates the use of gamification elements of badges, knowledge points (KPs), levels, and feedback to enhance reading comprehension. Thus, this study examines 50 undergraduate university students’ attitudes, motivation, autonomy and perceptions toward enhancing their reading skills. The data were collected from students’ interviews and questionnaires at YELI in KSA. The findings showed that students’ attitudes are high toward the gamified platform and that the utilization of Readtheory motivates students to read independently using the website inside and outside the classroom. The main advantage of Read theory is it can be easily used anywhere-anytime, a better alternative to traditional reading methods and it can be adopted to enhance student’s reading comprehension.
Problem of the Techno-subject: On the Subjectivity of “Entity-Constructors”

The article is devoted to the analysis of one of the most critical points of our time - the crisis of human subjectivity caused by the emergence of an alternative form of agent of social life in society - a techno-object with artificial intelligence and signs of subjectivity. The author’s goal is to justify the possibility of interpreting the nature of these devices as subject-like objects. The study is based on the explication of the concepts of the subject and subjectivity contained in both classical theories and the actor-network theory and related approaches (B. Latour, J. Law, A. Mol), in the object-oriented ontology of G. Harman, ‘onticology’ of Levi R. Bryant, theories of assemblage of M. DeLanda and complexity of E.Morin, in the concepts of the machine unconscious N. Land and techno-subject V. Mazin. An integrated characterization of a person as a subject is used: an individual being with active beginning, physicality, physicality and length, consciousness, personality characteristics and self-awareness. The criteria for categorizing some techno-objects as a type of techno-subject: autonomy and intelligence are introduced. Such techno-objects are artificial entities, assemblies, ready for transformation into techno-subjects. The difference between ‘weak’ techno-subjects (without artificial intelligence) and techno-subjects themselves (with artificial intelligence) is discussed. It is claimed that techno-subjects, genetically remaining technical entities, are in the transitional zone from one entity – ‘entity-constructor’ to another - organic techno-matter, similar to man. Accumulating components and functions of an active artificial system, techno-objects acquire the quality of subjectivity. It has been established that the artificial entity-subject manifests itself (a) in passive entity-actants; (b) in active proto-subject entities; and (c) actually in ‘entity-constructor’ - techno-subjects with the ability to integrate data, analyze it in a situation of uncertainty, reprogramming themselves and creating new algorithms.
Cloud Based Virtual Analysis of Patients and Automatic Assistance Using ML

Human Activity Recognition is one of the active research areas in computer vision for various contexts like security surveillance, health care and human computer interaction. Parkinson's disease is a brain disorder that leads to shaking, stiffness, and difficulty with walking, balance and coordination. Parkinson's symptoms usually begin gradually and get worse over time. As the disease progresses, people may have difficulty in walking and talking. The objective of the project is to train a machine-learning based model using Artificial Neural Network (ANN) and K-means algorithm. This model helps the patient with Parkinson’s disease to understand their actions and identify the error in their movements. The kinect camera captures the actions of the patient and transforms those into Gray scale image and depth view. The advantage of this system is to prevent the patient from going clinic thereby saving the time of physically challenged people. The proposed virtual Physiotherapist (PT) system has the potential of enabling on demand virtual care and significantly reducing cost for both patients and care providers.
Comparative Study of Breast Cancer Detection Methods

Breast cancer in women is the second-largest cause of cancer deaths. If it can be diagnosed early, it is also of the most curable forms of cancer.Breast cancer diagnosis is very confusing process. There are various methods used for diagnosis of cancer detection which will also increase uncertainty in detection. Late detection of cancer in patient or false detection both are not good for patient. In order to achieve correct detection of cancer in patients researchers are trying to use machine learning and deep learning algorithms for accurate detection of breast cancer.These algorithms will help doctors to detect breast cancer correctly and they can feel more confident about treatment .Various datasets,preprocessing methods, Several machine learning and deep learning methods and accuracy parameters are used for the detection of cancer summarized in this article.
IIIT_DWD@EACL2021: Identifying Troll Meme in Tamil using a hybrid deep learning approach

Social media are an open forum that allows people to share their knowledge, abilities, talents, ideas, or expressions. Simultaneously, it also allows people to post disrespectful, trolling, defamation, or negative content targeting users or the community based on their gender, race, religious beliefs, etc. Such posts are available in the form of text, image, video, and meme. Among them, memes are currently widely used to disseminate offensive material amongst people. It is primarily in the form of pictures and text. In the present paper, troll memes are identified, which is necessary to create a healthy society. To do so, a hybrid deep learning model combining convolutional neural networks and bidirectional long short term memory is proposed to identify trolled memes. The dataset used in the study is a part of the competition EACL 2021: Troll Meme classification in Tamil. The proposed model obtained 10th rank in the competition and reported a precision of 0.52, recall 0.59, and weighted F10.3.
The Essence and Composition Elements of Wisdom Education

The advancement of modern information technology has brought innovative ideas to education and has also supported education reform and development. At present, China’s education information level has significantly improved, providing strong support for wisdom education development. In our opinion, wisdom education is a very pertinent choice and essential trend of China’s education development in the information age. This article discusses the correct understanding of wisdom education, clarifies the basic structure of wisdom education, and makes several suggestions for the development of wisdom education.
Intrusion Detection System using Outlier Analysis

The tremendous advancement and increase in usage of internet have increased the number of intruders and hackers over the years. These cyber-attacks increase in complexity and sophistication day by day, and it is thus proposed that data mining be used to counter them. Data mining is one of the recent technologies suitably applied to intrusion detection to detect the network attacks, to reduce the complexities and to get normal behavioral pattern. In this paper, an outlier analysis model is introduced which can be effectively used for anomaly detection. This paper proposes a neighborhood-based technique for outlier detection, where the Level of Outlierness (LON) of network dataset is measured to detect the outliers, if any.
Contactless System with Mask and Temperature Detection

Coronavirus disease in 2019 has affected the world very badly on a large scale. One of the important protection methods is to wear masks in public areas. Also, while using public services it is important to wear a mask correctly if you want to use their services. However, there is very few researches on face mask detection based on image analysis. In this paper, we propose Face Mask, which is a high-accuracy and efficient face mask detector. The proposed system is a one-stage detector, which consists of a pyramid network to fuse high-level semantic information with multiple feature maps, and a module to focus on detecting face masks. In addition, we also propose a novel cross-class object removal algorithm that will reject predictions with low confidences and the high intersection of the union. Besides, we also focus on the possibilities of implementing Face Mask with a light-weighted neural network MobileNet for embedded or mobile devices. In this paper, we introduce an affordable solution aiming to increase COVID-19 indoor safety, covering relevant aspects: 1) contactless temperature sensing 2) mask detection. Contactless temperature sensing subsystem relies on Arduino Uno using an infrared sensor or thermal camera, while mask detection is performed by leveraging computer vision techniques and Deep Learning Techniques.
Forms and Consequences of the Cyber Threats and Extortion Phenomenon

Cyber threats and extortionate communications usually aim to influence the behavior or obtain compliance from victims. The phenomenon includes a wide range of forms, such as intimate partner violence; violence against women; dating abuse; sexual harassment; sexual coercion and extortion; etc. The victimization is linked to personal insecurity or disruption, mental and physical health, work or school performance, may represent a high financial burden, affecting negatively the sustainable development in a number of ways. The scale, scope, and challenges posed by this criminal phenomenon cannot be overstated. A deep analysis of the nature and extent of cyber extortion and threat is an essential component in the design of control strategies and programs. This research aims to provide an in-depth examination of the cyber threats and extortion phenomenon. The approach is interdisciplinary and empirically-informed. The analysis was done on a large number of cases and provides a comprehensive description of the essential characteristics of the phenomenon, including forms, communication vectors, and consequences for victims. The article concludes with recommendations for effectively addressing the phenomenon.
ORGANIZATION OF STUDENTS’ INDEPENDENT WORK THROUGH THE ACTIVE USE OF DISTANCE LEARNING TECHNOLOGIES IN TECHNICAL UNIVERSITIES

The article discusses the reasons for increasing the role of independent work of students on educational material and strengthening the responsibility of teachers for its successful implementation, as well as the principles of successful organization of students’ independent work are distinguished. Special attention is paid to the degree of efficiency and expediency of use of such creative task as the abstract in the educational system in modern conditions of Internet availability of any information. The organization of independent work of students is carried out through the active use of distance learning technologies implemented through the open information and educational environment of the University. Under such conditions the effectiveness of its organization determines the quality of the entire educational process and the formation of professional competencies
Personalized Virtual Mathematics System Based on K-mean Algorithm

As one of the core courses of primary and secondary education, mathematics is of great importance at home and abroad. However, how to stimulate students’ interest in mathematics learning and carry out individualized counseling in the classroom teaching environment has long been a difficult problem for mathematics educators. To improve their learning performance and reduce the burden of mathematics teachers. The research shows that the important performance of the system’s interaction, immediate feedback and positive motivation, are the important reasons for its positive influence. At the same time, the intelligent degree of the system under the genetic algorithm needs to be further improved to meet the personalized learning needs of many students.
Implementasi Algoritma Camellia Dalam Penyandian Pesan SMS Pada Telepon Seluler

SMS technology also does not guarantee the security and confidentiality of messages sent. The distribution of message content is very vulnerable to interference or theft by irresponsible parties, because the message content that is plaintext (text-light) can be read by anyone, to reduce the risk caused by one way of overcoming it is to implement a cryptographic algorithm on the message sent. The cryptographic algorithm used is the Camellia cryptographic algorithm with a 128-bit key length. Camellia is a block cipher designed by experts in research and development in cryptographic techniques. With the encrypted message sent then someone who managed to steal information about the SMS message sent, then someone who succeeded in stealing information from the text message sent would find it difficult to know the contents of the message. Designed and built an application program using Android with an Android system of at least version 2.2 to facilitate users in encoding SMS messages.
Reference Tools and Study Aids

Written by a team of experienced teachers of Spanish, this textbook is designed to lead the adult beginner to a comprehensive knowledge of Spanish, giving balanced attention to the four key language skills (speaking, listening, reading and writing). It puts language learning into its real-life context, by incorporating authentic materials such as newspaper articles, poems and songs. It contains a learner and a teacher guide and is intended to complement study both inside and outside the classroom, by providing pair and group activities, as well as materials for independent learning. It also includes helpful reference features, such as a guide to grammatical terms, verb tables, vocabulary lists and a pronunciation guide. This extensively updated second edition features extra exercises to support the acquisition of good pronunciation, and is accompanied by a web companion that hosts expansion exercises, activities, solutions and useful links for each unit, as well transcripts, and access to brand new recordings of all the audio examples found in the book.
Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models.

Emotion is fundamental to humanity. The ability to perceive, understand and respond to social interactions in a human-like manner is one of the most desired capabilities in artificial agents, particularly in social-media bots. Over the past few years, computational understanding and detection of emotional aspects in language have been vital in advancing human-computer interaction. The WASSA Shared Task 2021 released a dataset of news-stories across two tracks, Track-1 for Empathy and Distress Prediction and Track-2 for Multi-Dimension Emotion prediction at the essay-level. We describe our system entry for the WASSA 2021 Shared Task (for both Track-1 and Track-2), where we leveraged the information from Pre-trained language models for Track-specific Tasks. Our proposed models achieved an Average Pearson Score of 0.417, and a Macro-F1 Score of 0.502 in Track 1 and Track 2, respectively. In the Shared Task leaderboard, we secured the fourth rank in Track 1 and the second rank in Track 2.
The Challenging Landscape of Cloud Monitoring

This chapter reviews the concept of Cloud computing and its key features, its delivery options, and Cloud monitoring challenges in general. The authors review a few proposed solutions that other researchers have offered for these challenges. The authors argue that the critical nature of logs calls for tamper-proof and immutable log-storage and propose a platform on top of existing blockchains to store logs in the immutable blocks of a blockchain. The authors indicate the modules that are used, their order, the data flow, and the authors conclude this section by reviewing the architecture of the proposed solution. In this chapter, the authors highlight some of the challenges posed by the task of monitoring Cloud solutions, platforms, and systems. While some challenges have already been solved, authors proposed two solutions for two significant Cloud monitoring challenges that are still open, namely authentication of collected logs and the near-real-time log analysis.
Segway : Electric Hover Board

The Segway is an electronic transporter device which is battery powered and was invented by Dean Kamen. The Segway was first regulated in the US in 2002. It can be drive standing up and took one year of media promotion before its regulation. The device has wheels attached to a padlock that facilitates the standing of the rider. The control of the Segway remains in its handle and is easily adaptable. In order to ride the Segway successfully the rider must control the Segway wheels through the controls provided on each side of the handle. The device is easy to use and promotes the cause of Eco-Friendly Transportation.
Bach's Keyboard Music

Johann Sebastian Bach's reputation as one of the supreme figures of Western musical history rests primarily on the legacy of his keyboard music. Whereas the composer's church music and ensemble compositions, including such masterpieces as the Mass in B Minor, the Passions, and the Brandenburg Concertos, fell virtually into oblivion after his death, the Inventions and Sinfonias, the harpsichord suites, the Goldberg Variations, the chorale settings, and the preludes and fugues of The Well-Tempered Clavier have been the objects of unbroken study, veneration, and emulation by gen-erations of musicians—amateur and professional—from Bach's day to the present.