Reviewerly/exHarmony huggingface dataset:
Human evaluation of web-crawled parallel corpora for machine translation

Quality assessment has been an ongoing activity of the series of ParaCrawl efforts to crawl massive amounts of parallel data from multilingual websites for 29 languages.The goal of ParaCrawl is to get parallel data that is good for machine translation.To prove so, both, automatic (extrinsic) and human (intrinsic and extrinsic) evaluation tasks have been included as part of the quality assessment activity of the project.We sum up the various methods followed to address these evaluation tasks for the web-crawled corpora produced and their results.We review their advantages and disadvantages for the final goal of the ParaCrawl project and the related ongoing project MaCoCu.
Digital Platform for Virtual Music Concerts

Virtual music concerts on digital platforms are currently a hot topic of conversation.This virtual music concert is considered quite supportive because of the ongoing pandemic.This article describes how a virtual music concert is held on a digital platform.The purpose of writing this article is to describe clearly and in detail about how virtual music concerts are and how they are used using digital platforms.The method used in this research is descriptive qualitative with the results presented in the form of exposure.The data collection techniques used in this article are observation and literature study using related literature, both scientific journals, books and articles in the mass media.The results of the study revealed that some musicians and people behind the scenes of music concerts were affected by the COVID-19 pandemic.This of course makes musicians take advantage of digital platforms through the live streaming feature to hold virtual music concerts.In addition, virtual music concerts have become a pop culture for the Indonesian people, both now and in the future.
Challenges and Perspectives for Innu-Aimun within Indigenous Language Technologies

Innu-Aimun is an Algonquian language spoken in Eastern Canada.It is the language of the Innu, an indigenous people that now lives for the most part in a dozen communities across Quebec and Labrador.Although it is alive, Innu-Aimun sees important preservation and revitalization challenges and issues.The state of its technology is still nascent, with very few existing applications.This paper proposes a first survey of the available linguistic resources and existing technology for Innu-Aimun.Considering the existing linguistic and textual resources, we argue that developing language technology is feasible and propose first steps towards NLP applications like machine translation.The goal of developing such technologies is first and foremost to help efforts in improving language transmission and cultural safety and preservation for Innu-Aimun speakers, as those are considered urgent and vital issues.Finally, we discuss the importance of close collaboration and consultation with the Innu community in order to ensure that language technologies are developed respectfully and in accordance with that goal.
"A Historical Companion", or is a companion always a comrade? (Notes on the margins of the discussed the book)

The article is a summary of the content of the speech in the framework of the discussion of the book “Information: A Historical Companion”, which took place on the YouTube channel of the Association "History and Computer". The author analyzes the relevance of the topic of the book (the study of the history of the creation and dissemination of information in the historical past), its structure and content, the object and subject of research reflected in the articles of the authors of essay articles; chronological and territorial boundaries of the consideration of this topic; the source base and historiography that formed the basis of the articles in the collection, as well as the research methodology and factography presented in the book.As a result of the analysis, it is concluded that the authors of the articles included in the book adhere to the methodology of presentism, transferring modern ideas to the historical past, and in general, this book can be classified as a genre of "factoid historiography" based on unreliable facts or inaccurate interpretations, "convenient" for constructing the "necessary" concept of the development of the historical process for the authors of the book.
Figured Pronunciation in Grammatica Portoghesebrasiliana, by Gaetano Frisoni (1898)

The chapter entitled "Figured pronunciation in Grammatica Portoghese-Brasiliana, by Gaetano Frisoni (1898)" has its starting point in the work Grammatica ed esercizi pratici della lingua Portoghese-Brasiliana, written by
Research and Application of Film and Television Sound Dynamic Effect Design System Based on Artificial Intelligence

In the process of film from visual art to audio-visual art, sound design technology provides unlimited possibilities for its development.Before the emergence of audio film, we can only understand the connotation of the film through the actor's actions and some subtitles.Until the emergence of audio film, we can not only deeply understand the author's ideas through the actor's lines, the rendering and contrast of sound and music, but also let it better shape its own artistic works.The same is true of television, which appeared later than movies.The design of music, sound and language plays a very important role from the early sound design to the later sound image shaping and the second creation of the sound of the whole film.Therefore, this paper studies the film and television sound dynamic effect design system based on artificial intelligence.
Replacing Human Input in Spam Email Detection Using Deep Learning

The Covid-19 pandemic has been a driving force for a substantial increase in online activity and transactions across the globe. As a consequence, cyber-attacks, particularly those leveraging email as the preferred attack vector, have also increased exponentially since Q1 2020. Despite this, email remains a popular communication tool. Previously, in an effort to reduce the amount of spam entering a users inbox, many email providers started to incorporate spam filters into their products. However, many commercial spam filters rely on a human to train the filter, leaving a margin of risk if sufficient training has not occurred. In addition, knowing this, hackers employ more targeted and nuanced obfuscation methods to bypass in-built spam filters. In response to this continued problem, there is a growing body of research on the use of machine learning techniques for spam filtering. In many cases, detection results have shown great promise, but often still rely on human input to classify training datasets. In this study, we explore specifically the use of deep learning as a method of reducing human input required for spam detection. First, we evaluate the efficacy of popular spam detection methods/tools/techniques (freeware). Next, we narrow down machine learning techniques to select the appropriate method for our dataset. This was then compared with the accuracy of freeware spam detection tools to present our results. Our results showed that our deep learning model, based on simple word embedding and global max pooling (SWEM-max) had higher accuracy (98.41%) than both Thunderbird (95%) and Mailwasher (92%) which are based on Bayesian spam filtering. Finally, we postulate whether this improvement is enough to accept the removal of human input in spam email detection.
Bridging Static Site Generation with the Dynamic Web

Historically web sites have been developed using HTML for their markup either by authoring it directly or through abstraction to generate it. The currently available tools exist in a continuum of static, developer-oriented tools and dynamic services that cater to non-technical users. In this paper, we propose an approach that sits in the middle by using JSON for site definitions. The definition is leveraged on the client-side for editing, bridging the continuum’s ends.
Demand for Learning Technologies in Modern Education

Today, more than ever, education is becoming the most important area. Therefore, educational technologies, with the help of which teachers convey this or that material to their students, are of particular relevance. The purpose of the article is to describe the main educational technologies, provide their brief history and characteristics, and justify the demand for the main learning technologies in modern educational conditions. The authors come to the conclusion that some learning technologies are necessary and they are in demand in modern education. This article also reveals the concept of “learning technology».
Classification and Generation of Derivational Morpho-semantic Relations for Polish Language

In this article, we take a new look on automated analysis and recognition of morpho-semantic relations in Polish. We present a combination of two methods for join exploration on word-form information – generating new forms and classifying pairs of words in derivational relations. As a method of generation, we used the Transformer architecture in the seq-2-seq task. Classification is performed using a neural network and using the fastText representation method. At the very end, we discussed the results obtained in the experiments.
INSAR Community Newsletter: Tweets &amp; murmurs from Days 1 and 2 of the meeting

The lobby of the JW Marriott in Austin, Texas, was flush with scientists yesterday, as they arrived for the first in-person meeting of the International Society for Autism Research (INSAR) in three years.At the welcome session this morning, organizers announced that more than 1,800 people from 55 countries had registered, with about 1,000 ultimately arriving in person -numbers comparable to previous in-person gatherings.
Google and Apple Exposure Notifications System: Exposure Notifications or Notified Exposures?

On April 2020, Google and Apple announced the launch of a joint project: a system that promised to contribute to break COVID-19 contagion chains, called Exposure Notifications (EN). Countries around the world integrated EN within their public healthcare systems. This paper provides a critical inquiry on the legal and technical architecture of EN from a data protection law (DP) point of view. It is divided in two parts. In the first part we present EN as a proximity tracking tool, along with a technical description of its implementation, and a legal assessment of the contracts established between Google, Apple and governments (or public health authorities) regarding the design of national proximity tracking applications (apps). In the second part, the findings of the first part are critically discussed in light of the concepts of ‘legal by design’ and ‘legal protection by design’, building on Mireille Hildebrandt’s work. Through this conceptual approach, we examine the DP issues implied by EN’s embeddings and discuss the extent to which its design reveals a defiance to the rule of law. This contribution reiterates that the fundamental right to the protection of personal data covers both our individuality and our collective heritage of democracy, the rule of law and fundamental rights.
Role Of Crime Laboratories: Scope And Prospective In Criminal Investigation - Survey Based Analysis

Forensic Science is a discipline focused on gathering and analyzing evidences related to crime. It is carried out in a laboratory to provide unbiased report to the investigating agencies and to the judiciary. Forensic investigators collect genetic and non-genetic proof usually encountered in criminal cases. They do a careful analysis and prepare a report that aids the agencies in investigation. A Forensic Science Laboratory is a facility that carries out analytical work related to crime scenes. It works by analyzing the various types of evidential material found during a crime scene. This has been executed from the information gained from various already published research papers, conference proceedings and data available on the internet. The data is also collected from the survey conducted with the help of structured questions which have been asked in Google form online questionnaires on the subject matter.
Fail-Safe Controller for Assisting Users of Personal Mobility Robot with BMI

We have thus far presented a brain-machine-interface, BMI, for users of personal mobility robots. However, once the BMI predicts a wrong control command, both the user and robot face the danger of collision accidents. In this paper, therefore, we propose a fail-safe controller based on CNN (Convolutional Neural Network) for assisting users of personal mobility robots with the BMI. In addition to the control command, a depth map for the input image is simultaneously predicted by the fail-safe controller through multi-task learning. For this purpose, CAE (Convolutional Autoencoder) and DCGAN (Deep Convolutional Generative Adversarial Networks) are used instead of the CNN. In the experiments, we show that the fail-safe performance is increased by predicting the depth map for the input image. Finally, the fail-safe controller based on the DCGAN yields the best performance.
Different Optimizers-Based Gated Recurrent Unit Network to Forecast One Step Ahead Solar Irradiance

With the variable and stochastic behavior of climate and weather, the solar interconnected grids are prone to failure due to imbalanced operation, planning, and management. Therefore, precise and accurate solar irradiance forecasting becomes one of the important tasks in grid interconnected solar systems. This study is dedicated to the development of a solar forecasting model using the gated recurrent unit (GRU) deep learning network based on different optimizers. Three optimizers: adaptive moment estimation with momentum (ADAM), stochastic gradient descent with momentum (SGDM), and root mean square propagation (RMSprop) are used in the study with the GRU forecaster to forecast the one step ahead solar global horizontal irradiance (GHI). The developed models are trained with a one-year dataset of Indian location: Ahmadabad, Gujarat. Whereas, the monthly forecasting is performed using the trained model for discussed optimizers. To observe the performance of all models, root mean square error (RMSE) and mean absolute percentage error (MAPE) is used. In addition to this, the developed models are also compared with the naïve model considered as a baseline model. The results of the study show that the ADAM-GRU model outperforms SGDM-GRU, RMSprop-GRU as well baseline model.
Bridging the Gap between Recognition-level Pre-training and Commonsensical Vision-language Tasks

Large-scale visual-linguistic pre-training aims to capture the generic representations from multimodal features, which are essential for downstream vision-language tasks.Existing methods mostly focus on learning the semantic connections between visual objects and linguistic content, which tend to be recognitionlevel information and may not be sufficient for commonsensical reasoning tasks like VCR.In this paper, we propose a novel commonsensical vision-language pre-training framework to bridge the gap.We first augment the conventional image-caption pre-training datasets with commonsense inferences from a visuallinguistic GPT-2.To pre-train models on image, caption and commonsense inferences together, we propose two new tasks: masked commonsense modeling (MCM) and commonsense type prediction (CTP).To reduce the shortcut effect between captions and commonsense inferences, we further introduce the domain-wise adaptive masking that dynamically adjusts the masking ratio.Experimental results on downstream tasks, VCR and VQA, show the improvement of our pre-training strategy over previous methods.Human evaluation also validates the relevance, informativeness, and diversity of the generated commonsense inferences.Overall, we demonstrate the potential of incorporating commonsense knowledge into the conventional recognition-level visual-linguistic pre-training.
Data Visualisation Using Self-organising Maps

The utilization of Self-Organizing Maps (SOM) to reprieve various knowledge data and complex datasets. Self-Organizing Maps helps in data training, getting error metrics, and convergence properties exist in SOM. Visualizing a Self-Organizing Map on data set create a visual difference in retrieving information from data. Self-Organizing Maps are capable to deliver the information regarding locations of the high dimensional groups, it also can support to catch irregular forms, and provide the understanding about the outline of the dataset applied on.
COMPONENT MODEL OF THE CLOUD-ORIENTED LEARNING ENVIRONMENT OF A MEDICAL UNIVERSITY

The rapid development of innovative methods of learning, which are based on the widespread usage of information and communication technologies, forms the conditions for the transformation of the system of higher medical education. The emergence of cloud services in the IT services market has opened up new opportunities for the development of higher education institutions' (HEI) infrastructure. The quarantine crisis has made medical university faculty to begin restructuring the infrastructure of higher education institution in the direction of implementing the latest technologies of learning. During the COVID-19 quarantine period, medical universities started using Microsoft and Google cloud services extensively to provide distance learning to medical and pharmacy students. At the stage of adapting these services to the organization of the learning process, the problem was that there were no developed models of cloud-oriented learning environments for medical HEIs, which take into account the specifics of teaching subjects at medical faculties. In developing the component model, we applied methods of system analysis and approaches to describing the departments of the university from the perspective of an invariant model of the organizational system. This model allows scaling the technology of forming a hybrid cloud-oriented environment to all departments of the university. The component model of cloud-oriented educational environment was based on the model of the pedagogical system, which consists of the target, teaching, methodological, student, content and technological components. During the analysis of organization of medical education system of the university by method of stratification four main system levels were identified: І- strategic planning and organization of studies at the university; ІI- tactical planning and organization of medical education according to specialties; ІІI- planning and organization of educational process for students at 62 departments of the university; IV - modeling of partner interaction in the process of study on the basis of subject-subject relations of the teacher, technical staff of university departments and the student. The pedagogical system model was integrated with the hierarchical structural model of the medical university departments. Implementation of the developed model, in the shortest possible time, made it possible to optimally build the projection of the ground structure by means of MSO365 cloud services to the virtual cloud-oriented learning environment of the medical university and provide distance learning. The developed structure was implemented in the educational process of Zaporizhzhia State Medical University (ZSMU) during the period of COVID – 19 quarantine. The gained experimental experience in the organization of distance learning was used as the basis for the ZSMU mixed form of studying.
Logically at the Constraint 2022: Multimodal role labelling

This paper describes our system for the Constraint 2022 challenge at ACL 2022, whose goal is to detect which entities are glorified, vilified or victimised, within a meme .The task should be done considering the perspective of the meme's author.In our work, the challenge is treated as a multi-class classification task.For a given pair of a meme and an entity, we need to classify whether the entity is being referenced as Hero, a Villain, a Victim or Other.Our solution combines (ensembling) different models based on Unimodal (Text only) model and Multimodal model (Text + Images).We conduct several experiments and benchmarks different competitive pre-trained transformers and vision models in this work.Our solution, based on an ensembling method, is ranked first on the leaderboard and obtains a macro F1-score of 0.58 on test set.The code for the experiments and results are available at here .
A Comparative Analysis of Traditional Teaching and PBL Model

The "Teacher-Centered" approach is one of the most widely used teaching methods in traditional Chinese teaching.The teacher is mainly on imparting knowledge, and studentsare only playing the role of listening most time who hardly participate in the teaching process.By adopting this teaching method, teachers can have ensure class time and content management.However, for students, this teaching method limits their innovative thinking to a certain extent, and students are more likely to be distracted in class.It will become difficult for them to concentrate on the whole class when they lose interests.Different from traditional Chinese "Teacher-Centered" teaching methods, the "Student-Centered" teaching method can more easily mobilize students' enthusiasm for teaching participation and stimulate their innovative thinking.For example, the PBL teaching method mentioned in this paper can encouragestudents' active participation in teaching.It will be a good design if The teacher raises questions, and students think and discuss individually or in groups to get answers to the questions.This typical "Student-Centered" teaching method focuses on enabling students to become the center of the class.Then the teacher only guides students to think and encourages them to find the answers.At the same time, the "student-Centered" teaching method has high requirements for teachers to have awareness and fully understanding on the PBL theory.Due to limited Chinese current teaching resources, the limitations of traditional teaching methods and thinking inertia, it is impossible to adopt the "student-Centered" teaching method for teaching.Therefore, the article reviewsrelevant literature, compares the advantages and disadvantages between these two kinds of teaching methods, and taking considerations of the realistic background.We need constantly explore innovative breakthrough to the two kinds of teaching methods.The combination of implementation for breakthrough the traditional teaching will help to improve classroom teaching efficiency and students' acceptance, thereby cultivating the innovative students.
Analysis of the Current Situation of Research Management in Middle School Education in Domestic China and Overseas

Objective: To provide theoretical support for the construction and development of the Chinese localized management mode of middle school education and scientific research.Methods: With the theme of "Management of high school education and scientific research", related literature was retrieved and analyzed from domestic China databases, including CNKI Scholar and foreign databases.Results: There are few researchers in domestic middle schools, which mainly focus in qualitative research and the research depth is not enough, lacking of practical significance.In foreign countries, there are special educational and scientific research management institutions that adopt school-based management and cooperative management.Teachers and school administrators participate in educational research together.They tend to focus on practical problems, and the results are highly practical.Conclusion: The research level of educational scientific research management in Chinese middle schools is low, so we should learn advanced experience in foreign educational scientific research management and try to introduce various management theories, including knowledge management theory, to carry out localized research.