Reviewerly/exHarmony huggingface dataset:
Applying an Epidemiological Model to Evaluate the Propagation of Toxicity related to COVID-19 on Twitter

The prevalence of social media has increased the propagation of toxic behavior among users.Toxicity can have detrimental effects on users' emotion and insight and disrupt beneficial discourse.Evaluating the propagation of toxic content on social networks such as Twitter can provide the opportunity to understand the characteristics of this harmful phenomena.Identifying a mathematical model that can describe the propagation of toxic content on social networks is a valuable approach to this evaluation.In this paper, we utilized the SEIZ (Susceptible, Exposed, Infected, Skeptic) epidemiological model to find a mathematical model for the propagation of toxic content related to COVID-19 topics on Twitter.We collected Twitter data based on specific hashtags related to different COVID-19 topics such as covid, mask, vaccine, and lockdown.The findings demonstrate that the SEIZ model can properly model the propagation of toxicity on a social network with relatively low error.Determining an efficient mathematical model can increase the understanding of the dynamics of the propagation of toxicity on a social network such as Twitter.This understanding can help researchers and policymakers to develop methods to limit the propagation of toxic content on social networks.
Did the Covid-19 Pandemic Affect the Relationship Between Trading Volume and Return Volatility in the Cryptocurrencies?

In this study, it was investigated whether the Covid-19 pandemic, which started to affect the world in early 2020, influenced the relationship between return volatility and trading volume in the cryptocurrency market. In the empirical part of the study, 40 cryptocurrencies were included in the analysis. The data were divided into two separate periods as before and during the pandemic. Two alternative estimators developed by Garman and Klass (1980) and by Rogers and Satchell (1991) were used to measure the return volatility of cryptocurrencies. With causality and simultaneous correlation analyses, it was determined that the sequential information arrival hypothesis was valid in the cryptocurrency market in the pre-pandemic period. In the pandemic period, the sequential information arrival hypothesis lost its effect and left its place to the mixture of distribution hypothesis.
Cognitive digital platforms of scientific education

The digitalization of modern society, the emergence of the knowledge economy and the establishment of the principle of lifelong learning have led to the development of applied information and communication technologies in the educational process. One of the promising directions of the development of e-learning systems is the development of open educational systems using cognitive ICTs. The article discusses a knowledge-oriented approach to the formation of open educational systems based on the ontology and transdisciplinarity that provides the user with a holistic, systematic overview of a specific domain conceptualization of a specific knowledge area, represented by defining basic objects and connections between them. The authors describe cognitive software complex TEDAOS, which is intended for the construction of educational local and network (distributed) systems based on&nbsp; ontextsemantic analysis and ontologies (from the local ontology-driven system of the educational process provision to the system of integrated multifactor analysis of educational information resources through the decision-making system and management of knowledge formation process) to ensure the&nbsp; interaction of all users of online information and educational environments. The Web-software complex “Knowledge Base Ontological Scenarios Editor” for formalizing the process of preparation, conduct and assessment of learning outcomes using an e-scenario is presented.&nbsp
Bimbingan Belajar di Rumah Menggunakan Alat Peraga Blok Pecahan pada Masa Pandemi Covid 19

The impact of the COVID-19 pandemic is not only on the safety aspect of people's lives but also on the education aspect. One of the problems faced is that students have problems understanding each material given by the teacher because what has experienced so far is that students only get assignments online (What's App, E-Learning), then students do the assignments and send them back. At that moment, there is no direct explanation from the teacher, and each student's answer only relies on the internet (google), even though students need an understanding of the concepts in the material provided, especially mathematics for fractions. One of the activities that help students develop themselves in improving their learning abilities is the tutor. Solution offered to students who live in RT 003, Paupire Village, in this service activity is to provide Student learning assistance and guidance by using fractional block teaching aids during the Covid 19 Pandemic to increase understanding of concepts and learning outcomes. Tutoring can increase students' motivation and academic value, increase understanding of fraction concepts and learning outcomes.
Review of the National Style Research of Chinese Piano Solo Music in the 1980s

This paper takes the 1980s as the research horizon, with the Chinese piano solo national style as research, on the basis of collection, sorting, reading literature, chart to the 1980s Chinese piano solo national style research macro and dynamic theory, aims to promote their own understanding of Chinese piano solo national style problem, to lay a theoretical foundation for the future study of this subject.
Students' Perceptions in Using the Kahoot! Game on Reading Comprehension Learning

Reading comprehension is an activity to build people's understanding through texts by combining information obtained through texts with their knowledge and experience. Students face problems in reading comprehension, such as difficulty answering questions related to descriptive text, not being able to read the entire text, and confusing new vocabulary. This study aimed to determine how students perceive and improve reading comprehension after using Kahoot! Games as learning media for reading comprehension on description text at State Junior High School in Cilacap. This study uses an experimental sequential mix method with a qualitative descriptive method as the first order followed by a pre-experimental method using a one-group pretest-posttest design. Based on the results of the study, it was found that students' perceptions of using Kahoot! Games tend to be positive, students find learning fun, increase focus and caution, eliminate boredom, and motivate students to read the entire text. Furthermore, there was an increase in students' reading ability as indicated by the paired-sample t-test results where the sig value. the count obtained was 0.002 0.05, and the count obtained was 6.837 table 2.776. Supported by an 18% increase in the average student training results from 66.4 to 81.
Do Blockchain Stocks Exhibit More Herding Behaviour than Traditional Stocks?

While the volatile behaviour of cryptocurrency is extensively studied, the stock market’s blockchain sector, which has not been given much attention in the academic world, operates very differently from traditional stock industries. The paper hypothesizes that blockchain stocks exhibit more herding behaviour than traditional stocks and uses quantitative data analysis techniques to study it. The automotive industry is taken as a representative of traditional stocks. Cross-Sectional Absolute Deviation, the academic standard for herding behaviour, is used as the primary comparative measure between blockchain and automotive stocks. It reveals that blockchain industry has significant herding, while rational pricing mechanisms prevail in the automotive industry. Supporting this conclusion, a correlation matrix of stock prices of small market capitalisation firms in each industry is constructed, analysing how closely stock price movements in an industry are related. The correlation coefficient for blockchain stocks is 20% higher than the coefficient for automotive stocks. This indicates that blockchain stocks likely exhibit higher levels of herding. The impact of social media on stock price movements in the two industries is analysed by conducting a correlation study between Google Trends data for industry-related keywords and individual stock returns. The blockchain industry saw a significantly higher correlation, likely suggesting that social media has a stronger influence on blockchain stock price movements. Finally, the paper provides possible explanations for why herding behaviour is more prominent in the blockchain stocks compared to traditional stocks. These include absence of traditional stock valuation metrics, lack of financial knowledge and role of social media.
Contract-based Data-Driven Decision Making in Federated Data Ecosystems

The data-driven economy on the World Wide Web are based on coordination mechanisms for the exchange of and AI-based processing of data sets created by independent actors.Architectures for data-driven economic systems currently focus on the exchange of datasets and leave the processing of datasets to background mechanisms.Without binding commitments, agents in data sharing situations favor the "no data sharing" strategy.We present a broker framework that facilitates contract-based exchange, trading, and processing of data sets between agents under conditions of "lack of trust".Electronic contracts guarantee ownership and control of data datasets, the execution of defined data analysis tasks based on AI models, and the sharing of results according to contractual commitments.A technical architecture (TUCANA) is presented that provides a federated data economic ecosystem including a broker framework.We present an application based on an implementation of TUCANA.
LANGaware: Leveraging machine learning on natural language for the early detection of neurodegenerative and psychiatric diseases

Various neurodegenerative and psychiatric diseases are related with changes in spoken language [1], although they have seldom been investigated. We evaluate the effectiveness of our language-agnostic Machine Learning (ML) system to detect subtle changes in spoken language that manifest early signs of cognitive decline, thus assisting with its diagnosis. We evaluate our methodology using recordings of speech samples from multiple languages obtained from patient cohorts in early stages of cognitive decline and matched healthy controls.Our methodology involves capturing patient audio recordings while they are performing one or more predefined cognitive assessment tasks, involving, e.g., the description of a picture or recounting of an everyday activity. Afterwards, multi-language audio recordings and generated transcripts are analyzed with audio and NLP feature extraction methods [3], ranging from semantic, morpho-syntactic, phonological representations of the input, as well as, more sophisticated linguistic measures. The feature pool is filtered by a Pearson's rho threshold of 0.85. We build a Random Forest classifier out of 100 Decision trees, using the Gini impurity criterion, 5-fold cross-validation for training, elimination and composition-based feature selection, as well as post-selection retraining / fine-tuning. The model's diagnostic performance is evaluated on a test set unseen during training.Our results are validated against the diagnosis that is provided by medical experts. Our performance in terms of accuracy (∼82%), f1 (84%) and ROC-AUC score (∼82%) are clear indicators of the effectiveness of speech analysis towards detecting cognitive decline. Moreover, our tree-based classifier produces probability scores that closely follow the proportion of pathological cases in the input data, with a correlation of 94%.In the current evaluation we verified our conjectures regarding the strong capacity of speech to predict cognitive decline. Audio analysis and machine learning are proven to be invaluable tools in the prediction of early signs of cognitive decline, which are coupled with a wide spectrum of neurodegenerative and psychiatric diseases. [1] Boschi, Veronica, et al., Frontiers in psychology 8 (2017): 269. [2] Vassiliki Rentoumi et al., Alzheimer's & Dementia, Wiley, volume 16, 2020. [3] Alberdi, Ane et al., Artificial intelligence in medicine 71 (2016): 1-29.
An Autonomous Discord Bot to Improve Online Course Experience and Engagement: Lessons Learned Amid the COVID-19 Pandemic

The COVID-19 pandemic pushed many educational institutions to adopt online learning models for most or all of their courses.As a result, the effectiveness of remote learning is more important now than ever before.In this paper, we report on work that was conducted in the Spring of 2021 at Utah Valley University.We explored the use of Discord as a delivery mechanism for online course content during the 2020-2021 school year.We also developed a Discord bot to autonomously track attendance.Based on our experience to date, the Discord bot appears to enhance remote learning.We describe the design, implementation, and deployment of our bot.We also discuss what worked well, as well as areas for improvement.In future semesters we plan to collect data by which we may begin to answer fundamental questions about the impact of such bots on remote learning.
Analysis of Human-Machine Interaction Through Facial Expression and Hand-Gesture Recognition

This paper focuses on a review of recent work on facial expression and hand gesture recognitions. Facial expressions and hand gestures are used to express emotions without oral communication. The human brain has the ability to identify the emotions of persons using expressions or hand gestures within a fraction of a second. Research has been conducted on human–machine interactions (HMIs), and the expectation is that systems based on such HMI algorithms should respond similarly. Furthermore, when a person intends to express emotions orally, he or she automatically uses complementary facial expressions and hand gestures. Extant systems are designed to express these emotions through HMIs without oral communication. Other systems have added various combinations of hand gestures and facial expressions as videos or images. The meaning or emotions conveyed by particular hand gestures and expressions are predefined in these cases. Accordingly, the systems were trained and tested. Further, certain extant systems have separately defined the meanings of such hand gestures and facial expressions
Local Model-Agnostic Explanations for Black-box Recommender Systems Using Interaction Graphs and Link Prediction Techniques

Explanations in recommender systems are a requirement to improve users' trust and experience.Traditionally, explanations in recommender systems are derived from their internal data regarding ratings, item features, and user profiles.However, this information is not available in black-box recommender systems that lack sufficient data transparency.This current work proposes a local model-agnostic, explanation-by-example method for recommender systems based on knowledge graphs to leverage this knowledge requirement.It only requires information about the interactions between users and items.Through the proper transformation of these knowledge graphs into item-based and user-based structures, link prediction techniques are applied to find similarities between the nodes and to identify explanatory items for the user's recommendation.Experimental evaluation demonstrates that these knowledge graphs are more effective than classical contentbased explanation approaches but have lower information requirements, making them more suitable for blackbox recommender systems.
Design and Implementation of DDS Module on FPGA

he paper concerns the construction scheme of Direct Digital Synthesis (DDS) generator based on widely developed Field Programmable Gate Arrays (FPGA) technology. based on (DDS) it generates sine wave that frequency and phase is manageable is designed with direct digital synthesis(DDS) technology. It is showed that the design based on FPGA with DDS is dependable and practicable. The output wave by test reaches the essential aims, easy control and high performance. The DDS produce sinusoidal signal owns the features of modest circuit, easy to be measured, unchanging performance, high frequency conversion speed and fine accuracy etc. And its output frequency falls within the range of 0Hz ~ 150KHz with 5 Hz of steps
Comparison of Sampling Methods Using Machine Learning and Deep Learning Algorithms with an Imbalanced Data Set for the Prevention of Violence Against Physicians

The aim of this study is to compare sampling methods using machine and deep learning algorithms with a small and imbalanced data set for the prevention of violence against physicians. In this data set, it is determined whether there is violence against physicians by using various demographic information of physicians. In addition, in this study, it is tried find effective solutions to improve the working conditions of physicians in order to reduce violence against physicians. As a solution to the imbalanced data problem, Synthetic Minority Oversampling (SMOTE), Random Oversampling (ROS) and Random Undersampling (RUS) methods were used to balance the data in this study. Then, Random Forest Classifier (RFC), Extra Tree Classifier (ETC) and Multi-Layer Perceptron (MLP) algorithms were applied. Among all sampling techniques and classification algorithms, the ETC algorithm applied with the ROS method shows the best performance with 82% accuracy and 0.81 F1-Score.
Perceived Sensations in Architectural Spaces through Immersive Virtual Reality

To design architectural spaces that not only respond to the basic needs of users, but also seek their emotional well-being, it is necessary for the architecture students to have a special sensitivity and be aware of the different sensations that their designs should and can evoke. To achieve this competence without exploring real spaces, Immersive Virtual Reality technology offers an important contribution to the field of architecture. The purpose of this research is to determine if the sensations perceived in virtual architectural spaces by students are similar to the real ones and to determine the characteristics of this technology that allow a better perception of sensations. Six architectural modules were designed to be walked through and experienced at real scale using a Head Mounted Display by 22 students of the first and fifth year of studies of Architecture career in Peru. An ad-hoc questionnaire allowed to know the perceived sensations and the benefits of the tool. The results obtained showed that the perception of sensations of the fifth year students is a little closer to those expressed by a group of seven experts compared to that of the first year students and that the students consider the characteristics of accessibility, real scale of the space and the possibility of going through and looking at the space in all directions are those that have given more realism to the experience and therefore better perception of the space, while the characteristics of natural light and shadows, construction materials and external environment have been less valued in the realism of the experience. It is concluded that the sensory experimentation in architectural spaces modelled realistically in virtual environments allows the perception of sensations very similar to those that the architect seeks to convey initially.
Discrete semantic embedding hashing for scalable cross-modal retrieval

Cross-modal hashing has attracted much attention for cross-modal retrieval and achieved promising performance due to its powerful capacity. Some existing cross-modal hashing methods construct pairwise similarities to represent the relationship of heterogeneous data, which require much computation time and storage space, making them unscalable for large-scale retrieval tasks. In this paper, we propose a novel supervised Discrete Semantic Embedding Hashing (DSEH) for cross-modal retrieval. Specifically, we first learn the common representation of heterogeneous data by embedding the semantic labels into a collective matrix factorization, such that both intra- and inter-modality similarities can be well captured. Then, we learn the hash codes in the discrete space based on the learned common representation via an orthogonal rotation technique. Moreover, we learn the multi-modal hash functions that can efficiently convert out-of-sample instances into unified hash codes. Extensive experimental results on three widely used benchmark databases demonstrate the superiority of the proposed DSEH compared with previous state-of-the-arts.
ANALYSIS OF TEACHERS’ MANAGEMENT AND INSTRUCTIONAL TALK IN ENGLISH FOREIGN LANGUAGE (EFL) CLASSROOM AT SMA NEGERI 1 BOMBANA

This study aims were to determine the context of teacher management and instructional talks in senior high schools, especially in Senior High Schools of 1 Bombana and the frequency of use of teachers' native language (NL) and target language (TL). The study subjects were two Indonesian English teachers. The descriptive qualitative method was used to obtain and analyze data. Our study results showed four contexts of utterance related to management talk while three contexts were related to the instructional talk. Teachers prefer to use the native language than the target language in either management talk term or instructional talk term. The frequency of using the native language was much higher both in management and instructional talk. It indicated by 86.9% average used in management talk, while 77.1% in the instructional talk. In addition, the reason for the domination of the native language was to reach the effectiveness of learning in EFL classes. However, it was also implied that the teacher's competence to use English in the classroom was inadequate.
Progressive Residual Learning With Memory Upgrade for Ultrasound Image Blind Super-Resolution

For clinical medical diagnosis and treatment, image super-resolution (SR) technology will be helpful to improve the ultrasonic imaging quality so as to enhance the accuracy of disease diagnosis. However, due to the differences of sensing devices or transmission media, the resolution degradation process of ultrasound imaging in real scenes is uncontrollable, especially when the blur kernel is usually unknown. This issue makes current end-to-end SR networks poor performance when applied to ultrasonic images. Aiming to achieve effective SR in real ultrasound medical scenes, in this work, we propose a blind deep SR method based on progressive residual learning and memory upgrade. Specifically, we estimate the accurate blur kernel from the spatial attention map block of low resolution (LR) ultrasound image through a multi-label classification network, then we construct three modules–up- sampling (US) module, residual learning (RL) model and memory upgrading (MU) model for ultrasound image blind SR. The US module is designed to upscale the input information and the up-sampled residual result will be used for SR reconstruction. The RL module is employed to approximate the original LR and continuously generate the updated residual and feed it to the next US module. The last MU module can store all progressively learned residuals, which offers increased interactions between the US and RL modules, augmenting the details recovery. Extensive experiments and evaluations on the benchmark CCA-US and US-CASE datasets demonstrate the proposed approach achieves better performance against the state-of-the-art methods.