Reviewerly/exHarmony huggingface dataset:
To “Matar a Bola no Peito” and “Fazer um Golaço” in the Speech: Predicate with (Semi-)Support Verb

The linguistic object of this chapter is a sample of Brazilian expressions composed by a (semi-)support verb and a nonverbal element that, together, forms a complex predicate indicative of state of affairs that is related to a sportive play, to soccer domain.Some examples of these expressions are exemplars such as "fazer um golaço" (something like "make a great goal"), "dar um carrinho" (more or less in the sense of "disarming the opponent´s ball domain with one-legged play"), "fazer uma defesa" ("make a defense") and "marcar um gol" ("score a goal").Some of them are made up of nonverbal elements which contain degree affixes ("fazer um golaço", "fazer um golzinho").The complex (verb-noun/verbal-nominal) predicate indicates a (dynamic or non-dynamic) state of affairs and then tends to select one participant role or more, matching, by force of attraction or coercion, to a construction of argument structure (usually a grammatical person structure that
The Impact of Quantitative Easing on Bank Lending

This article aims to examine and compare the essential effects of quantitative easing policies in the United States on bank lending.First of all, the paper examines the overall credit scale from a macro perspective, but different results were presented before and after the quantitative easing policy in the three periods.Second, consider the micro view and utilize several specific data points to analyze the significant changes before and after the three approaches.By examining the extent of credit, coupled with the unemployment rate, house prices, and commodity markets, one can trace their farreaching effects.As a whole, the quantitative easing policy is of great importance to the sluggish financial need, and it has been used by many countries successively.Under the heavy impact of this epidemic in the past two years, the United States once again instituted a quantitative easing policy in an effort to maintain the stability of the financial markets.
Research on upstream design support using machine learning (Extraction of pleasant factors from engine and electric motorcycle driving data)

The purpose of this study is to investigate a machine learning method to support upstream of design process by taking a motorcycle design targeting riding comfort as an example. In our previous report, a machine learning decision tree model was trained using temporal factors with the riders’ subjective evaluation as the target variable while the physical and dynamic states of the bike and the riders were used as predictors. In the present study, a high performance deep-learning regression neural network was trained to predict the real-time subjective evaluation of riders using physical state data captured over a certain time period. Deep learning algorithms, however, often yield black box models which are inadequate for the purpose of acquiring design knowledge. Hence, the feature importances of the models were extracted using explainable artificial intelligence (XAI). Relationships between physical states and changes in rider’s pleasure were elucidated by applying XAI on the deep learning model and bike riding data. As the result, two design guidelines to increase riding comfort were obtained as (1) improve the throttle feel and responsiveness, and (2) improve vibration control at the rear of the motorcycle and rear suspension.
The problem of critical thinking during media content consumption in the conditions of an information war

The article is devoted to the problem of content consumption in modern media, in the context of the development of critical thinking, the basic components of which are media literacy and information stability. The urgency of the study lies in the fact that today there are serious threats for the Ukrainian media and information fields, which arose as a result of the hybrid geopolitical aggression that Ukraine has suffered from, from the Russian Federation. The subject of the presented research is critical thinking and the skills of resisting manipulations in the media in the servicemen of the Ukraine Army. The purpose of the study presented in this article is to quantify the level of critical thinking in the target group and identify specific issues in this context. To achieve this goal, such general scientific methods as analysis, comparison and observation were used, and the main applied research was a sociological survey (questionnaire). The results of the study show that the main sources of information for the representatives of the target group are social networks (79.8 %), online media (61.5 %), and messengers (46.8 %). The vast majority of respondents (70 %) used Ukrainian when consuming media content, 22 % of them used no language, and 8 % admitted that they prefer Russian. It was found that the target group chose more visual forms (video, infographics, photos) than texts as the media content format. Unfortunately, the study revealed low levels of critical thinking, as only 53.7 % said they would check information obtained from the media in alternative sources. 45.1 % of respondents believed that they had never been the object of media manipulation, while in fact the propaganda of the relevant structures of the Russian Federation is primarily aimed at them. The results of the study show that the representatives of the target group have a very serious problem with critical thinking, the level of media literacy and information stability. This circumstance is a prerequisite for the need to further improve the management of information processes in the Ukrainian media for the Armed Forces, qualitatively improve and increase the production of relevant media content, as conducting systematic training for target groups on media literacy and information sustainability.
Fusing Census and MC-CNN Cost Volumes for Stereo Matching

Stereo matching is an important and popular field of computer vision.Numerous researchers worldwide are devoted to enhancing the effectiveness of stereo matching applications.In stereo matching, determining the costs of matching is a critical step.This step generates a cost volume that quantifies the similarity of pixels, and thereafter, it is processed further to generate the final disparity map.The purpose of this study is to improve stereo matching performance by fusing two different cost-volumes, namely Census and MC-CNN.The Census transform and Hamming distance are one of the most frequently used cost functions in conventional approaches.Besides, a matching cost volume generated using a deep learning technique called MC-CNN has been shown to extract more reliable features from images than conventional approaches.Thus, both of these cost computation strategies have a number of advantages and disadvantages.By including deep learning as a cost-volume, the advantages of these two distinct cost-volumes complement one another, resulting in a better cost-volume prior to applying the smoothing operation (e.g.Semi-global matching or More-global Matching).Our findings indicate that fusing costvolumes improves the stereo matching performance of nearly all of the benchmark stereo images we tested in the Middlebury dataset.
Flexible Generation from Fragmentary Linguistic Input

The dominant paradigm for high-performance models in novel NLP tasks today is direct specialization for the task via training from scratch or fine-tuning large pre-trained models.But does direct specialization capture how humans approach novel language tasks?We hypothesize that human performance is better characterized by flexible inference through composition of basic computational motifs available to the human language user.To test this hypothesis, we formulate a set of novel fragmentary text completion tasks, and compare the behavior of three direct-specialization models against a new model we introduce, GibbsComplete, which composes two basic computational motifs central to contemporary models: masked and autoregressive word prediction.We conduct three types of evaluation: human judgments of completion quality, satisfaction of syntactic constraints imposed by the input fragment, and similarity to human behavior in the structural statistics of the completions.With no task-specific parameter tuning, GibbsComplete performs comparably to direct-specialization models in the first two evaluations, and outperforms all directspecialization models in the third evaluation.These results support our hypothesis that human behavior in novel language tasks and environments may be better characterized by flexible composition of basic computational motifs rather than by direct specialization.
A Survey on MCT vs. DCT: Who is the Winner in COVID-19

Coronavirus disease (COVID-19) is a contagious disease appeared in late 2019 and caused by a virus called SARS-CoV-2.It is a pandemic spreading across the whole world and impacts millions of people and sadly causes death.There are two main Contact Tracing Methods (CTMs) to limit and slow down any chance of transmission of it: Manual Contact Tracing (MCT) and Digital Contact Tracing (DCT).The MCT abides by the guide to World Health Organization's guidance (WHO) on COVID-19 in terms of properly applying social distancing,wearing masks, washing hands, using sanitizers, etc. while the DCT abides by the digital contact tracing applications developed by several countries.This survey is mainly focused on these CTMs and the recent proposed solutions in this field, in order to highlight their drawbacks that negatively impact on both of satisfaction and feasibility in using them.The findings in the survey will be beneficial to understand the effectiveness of CTMs and current proposed solutions, in order to develop a comprehensive smart tracking system able to cooperatively contribute with both of MCT and DCT in extremely detecting, preventing, and slowing down the spread of COVID-19 or even any other similar pandemics in the future.
Management decisions: approaches to adoptation and implementation in an educational organisation

The article considers approaches to management decisions in an educational organisation. Special attention is given to the key factors in making and implementing management decisions. The authors emphasize that decision making is a complex process including a few logical steps presented in the article. The article justifies styles of decision making which include: autocratic, persuasive, consultative, cooperative; shows key actions and stakeholders. The evidence is given that the adequacy of the effectively implemented decision depends on a clearly defined sequence of steps and on the members of the management team responsible for the implementation of the decision. The conclusion is made that to minimize negative effects under the decision making process it is necessary to apply effective techniques for qualitative consideration of the problem at a rational level. The literacy and effectiveness of decision making depend on a plan of actions, analysis of progress in solving the problem, as well as building clear relationships between the members of the management team who are responsible for the decision-making process. The important conclusion in the article is that the effective implementation of decisions depends on the ability of the head of the educational organisation to adapt and take technological and social changes.
Design of Internet of Things Analysis and Management System for Quality Data

Through the application of data mining technology and database technology, this paper designs and develops a three-tier framework of B / s technology for college students' quality analysis and management system.System development software environment is as follows: SQL Server 2005 database, C language and ASP.NET The overall function of the technical department is divided into four modules, namely, physical examination management, sports performance management, statistical analysis and basic file data management.The construction of these four modules makes the analysis of sports quality information.Using data mining technology and decision tree algorithm to complete the collection of students' physical education results, and provide data for the next step to improve students' physical fitness.This paper involves the development of system analysis, system design, system coding and testing and the application of related knowledge.
FROM THE HISTORY OF GEOGRAPHICAL RESEARCH OF TEACHERS OF ULYANOVSK STATE PEDAGOGICAL UNIVERSITY

The article is devoted to a brief review of geographical research at the Ulyanovsk State Pedagogical University.
Intelligent system of information support of the education process to improve student performance

This article discusses the possibility of creating and developing a personal educational environment as a tool to improve student performance in higher educational institutions. The task of this system is to track what is happening with the student in the educational institution and promptly inform him and the University management about further problems in educational activities.
Domain Human Recognition Techniques using Deep Learning

As a key research subject in the fields of health and human-machine interaction, human activity recognition (HAR) has emerged as a major research focus over the past few decades.Many artificial intelligence-based models are being created for activity recognition.However, these algorithms are failing to extract spatial and temporal properties, resulting in poor performance on real-world long-term HAR.A drawback in the literature is that there are only a small number of publicly available datasets for physical activity recognition that contain a small number of activities, owing to the scarcity of publicly available datasets.In this paper, a hybrid model for activity recognition that incorporates both convolutional neural networks (CNN) are developed.The CNN network is used for extracting spatial characteristics, while the LSTM network is used for learning time-related information.Using a variety of traditional and deep machine learning models, an extensive ablation investigation is carried out in order to find the best possible HAR solution.The CNN approach can achieve a precision of 90.89%, indicating that the model is suitable for HAR applications.
Action Recognition in Australian Rules Football Through Deep Learning

Understanding player’s actions and activities in sports is crucial to analyze player and team performance. Within Australian Rules football, such data is typically captured manually by multiple (paid) spectators working for sports data analytics companies. This data is augmented with data from GPS tracking devices in player clothing. This paper focuses on exploring the feasibility of action recognition in Australian rules football through deep learning and use of 3-dimensional Convolutional Neural Networks (3D CNNs). We identify several key actions that players perform: kick, pass, mark and contested mark, as well as non-action events such as images of the crowd or players running with the ball. We explore various state-of-the-art deep learning architectures and developed a custom data set containing over 500 video clips targeted specifically to Australian rules football. We fine-tune a variety of models and achieve a top-1 accuracy of 77.45% using R2+1D ResNet-152. We also consider team and player identification and tracking using You Only Look Once (YOLO) and Simple Online and Realtime Tracking with a deep association metric (DeepSORT) algorithms. To the best of our knowledge, this is the first paper to address the topic of action recognition in Australian rules football.
Handling Comments in Documents through Interactions

Comments are widely used by users in collaborative documents every day. The documents’ comments enable collaborative editing and review dynamics, transforming each document into a context-sensitive communication channel. Understanding the role of comments in communication dynamics within documents is the first step towards automating their management. In this paper we propose the first ever taxonomy for different types of in-document comments based on analysis of a large scale dataset of public documents from the web. We envision that the next generation of intelligent collaborative document experiences allow interactive creation and consumption of content, there We also introduce the components necessary for developing novel tools that automate the handling of comments through natural language interaction with the documents. We identify the commands that users would use to respond to various types of comments. We train machine learning algorithms to recognize the different types of comments and assess their feasibility. We conclude by discussing some of the implications for the design of automatic document management tools.
Simulation of random photon loss in boson sampling of different optical networks

Boson sampling is a candidate for quantum protocols to truly realize the quantum computation advantage and to be used in advanced fields where complex computations are needed, such as quantum chemistry. However, this proposal is hard to achieve due to the existence of noise sources such as photon losses. In order to quantificationally analyze the influences of photon losses in optical networks, boson sampling is classically simulated based on the equivalent beam splitter mechanism, where the photon loss happening in optical units is equivalent to the photon transmission into the environmental paths through a virtual beam splitter. In our simulation, networks corresponding to random unitary matrices are made up, considering both the Reck structure and the Clements structure. The photon loss probability in an optical unit is well controlled by adjusting the parameters of the virtual beam splitter. Therefore, to simulate boson sampling with photon losses in optical networks is actually to simulate ideal boson sampling with more modes. It is found that when the photon loss probability is constant, boson sampling with Clements structures distinctly performs much better than that with Reck structures. Furthermore, the photon loss probability is also set to follow the normal distribution, which is thought to be closer to the situation in reality. It is found that when the mean value of photon loss probability is constant, for both network structures, errors of outputs become more obvious with the increase of standard deviation. It can be inferred that the increase of error rate can be explained by the network depth and the conclusion is suitable for larger-scale boson sampling. Finally, the number of output photons is taken into consideration, which is directly related to the classical computation complexity. It is found that with the photon loss probability, the ratio of output combinations without photon losses decreases sharply, implying that photon losses can obviously affect the quantum computation advantage of boson sampling. Our results indicate that photon losses can result in serious errors for boson sampling, even with a stable network structure such as that of Clements. This work is helpful for boson sampling experiments in reality and it is desired to develop a better protocol, for example, a well-designed network or excellent optical units, to well suppress photon losses.
Vacillating Human Correlation of SacreBLEU in Unprotected Languages

SacreBLEU, by incorporating a text normalizing step in the pipeline, has become a rising automatic evaluation metric in recent MT studies.With agglutinative languages such as Korean, however, the lexical-level metric cannot provide a conceivable result without a customized pre-tokenization.This paper endeavors to examine the influence of diversified tokenization schemes -word, morpheme, subword, character, and consonants & vowels (CV)-on the metric after its protective layer is peeled off.By performing meta-evaluation with manuallyconstructed into-Korean resources, our empirical study demonstrates that the human correlation of the surface-based metric and other homogeneous ones (as an extension) vacillates greatly by the token type.Moreover, the human correlation of the metric often deteriorates due to some tokenization, with CV one of its culprits.Guiding through the proper usage of tokenizers for the given metric, we discover i) the feasibility of the character tokens and ii) the deficit of CV in the Korean MT evaluation. 1 Recently, the research topic of word segmenta-
Exploring the life and works of Roberto Gerhard, the electronic music pioneer

DIGITISATION -the process of converting analogue media into a digital format ELECTRONIC MUSIC -any music involving electronic processing, such as recording or editing MIXER -sometimes called a mixing desk, a device that can take various audio sources and combine them to produce different outputs MODERNISM -in music, a broad time period encompassing much of the 20th century, involving innovative approaches to reinterpreting 'traditional' musical techniques SYNTHESIS -the process of creating sounds using electronic signals TEXTURE -in music, how different aspects are combined in composition to determine an overall quality of sound TIMBRE -the 'tone colour', or perceived sound quality, of a musical note WHILE WE MIGHT THINK OF ELECTRO NIC MUSIC AS A NEW STYLE THAT HAS EMERGED IN