Reviewerly/exHarmony huggingface dataset:
Conceptual Metaphor in Daily Spiritual Texts Murli by Brahma Kumaris

The meaning of the metaphor is understanding as one of the conceptual domain in terms of other conceptual domains. A metaphor is used effortlessly in everyday life, including in a literary work. The aim of this study is able to help people to understand the metaphoric meaning of Murli, a spiritual text written by Brahma Kumaris. The problems of this study discussed based on metaphorical expressions used in daily spiritual texts Murli and the conceptual metaphors motivate the application of the metaphorical expressions in daily spiritual texts Murli. The data of this study is taken from the daily spiritual texts by Brahma Kumaris Murli. The technique that is used to collect the data is the note-taking technique. The metaphor is identified using MIP (Metaphor Identification Process) by Pragglejaz Group This method helps the process of analyzing metaphorical words. The result of this study showed that Murli consists of 16 conceptual metaphors of the structural metaphor, 2 conceptual metaphors of the orientational metaphor, and 3 conceptual metaphors of the ontological metaphor including the personification and 1 conceptual metaphor of the metonymy.
A Mixture-of-Expert Approach to RL-based Dialogue Management

Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use reinforcement learning (RL) to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with a combinatorially complex action space even for a medium-size vocabulary. As a result, they struggle to produce a successful and engaging dialogue even if they are warm-started with a pre-trained LM. To address this issue, we develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of {\em specialized} LMs (or experts) capable of generating utterances corresponding to a particular attribute or personality, and (iii) a RL-based DM that performs dialogue planning with the utterances generated by the experts. Our MoE approach provides greater flexibility to generate sensible utterances with different intents and allows RL to focus on conversational-level DM. We compare it with SOTA baselines on open-domain dialogues and demonstrate its effectiveness both in terms of the diversity and sensibility of the generated utterances and the overall DM performance.
Implementation Of Google Classroom Application In Islamic Religious Education Learning At SMK Raden Rahmat Mojosari Mojokerto

This researched aims to : 1) learning plans of Islamic education to use google classroom in vocational Raden Rahmat, 2) learning Islamic education by using google classroom in vocational Raden Rahmat, 3) evaluation of learning Islamic Education by using google classroom at SMK Raden Rahmat. Based on research that has been conducted, the results showed by choosing google classroom as a learning medium. The First Planning for google classroom at SMK this Raden Rahmat doing a joint meeting during the 19th COVID beginning of each semester through zoom and offline applications, setting up RPP PAI, Develop the PAI syllabus, prepare learning media in accordance with the respective fields of the teacher, There is a circular from Nadiem Makarim. Second2. Proses Islamic religious education learning to use google classroom in vocational Raden Rahmat is to prepare the course material in advance, before use google classroom but before I directed learning in advance using watshapp that it will immediately begin learning, students study the learning material given by the teacher then do their work, educators sometimes give a deadline of one week or three days to submit the assignment to google classroom or e-mail. 3. Evaluation of Islamic Religious Education learning by using google classroom at Raden Rahmat Vocational School is carried out once a week with material being given, the following week given assignments, from that task we can assess the activity of children in participating in online learning.&#x0D; Keywords: Implementation, Google Classroom, Islamic Religious Education Learning.
Information-Theoretic Policy Learning from Partial Observations with Fully Informed Decision Makers

In this work we formulate and treat an extension of the Imitation from Observations problem. Imitation from Observations is a generalisation of the well-known Imitation Learning problem where state-only demonstrations are considered. In our treatment we extend the scope of Imitation from Observations to feature-only demonstrations which could arguably be described as partial observations. Therewith we mean that the full state of the decision makers is unknown and imitation must take place on the basis of a limited set of features. We set out for methods that extract an executable policy directly from those features which, in the literature, would be referred to as Behavioural Cloning methods. Our treatment combines elements from probability and information theory and draws connections with entropy regularized Markov Decision Processes.
CONTENT OF EMOTIONAL CATEGORY CULTURE OF PERSONALITY ”IN MODERN PEDAGOGICAL RESEARCH

The author of the article considérés the essence of the category “emotional culture ”. A brief analysis of pedagogical writing in the given problem is adduced in the article.
GOVERNANÇA MUNICIPAL - IMPACTOS NA MITIGAÇÃO DA PANDEMIA COVID-19: ESTUDO EM UM MUNICÍPIO BRASILEIRO

The present society that is globalized and digitized requires companies that deliver, among other factors, trustworthiness and easiness to their target market.More and more these organizations have their challenges of creating strategies and creating actions that generate a long term relationship with their stakeholders 1 , in a way that strengthens the brand and impacts the behavior of the customer when they are choosing a product and/or service.For that reason this paper was in need to understand the actions of Public Relations generate value to a startup's branding 2 .The results demonstrate the level of understanding of the partners of the studied company X¹ and the choice of the final customer, Human Resources professionals, as well as their personal characteristics.
FSSA: Efficient 3-Round Secure Aggregation for Privacy-Preserving Federated Learning

&lt;p&gt;Federated learning (FL) allows a large number of clients to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data. This paper proposes a 3-round secure aggregation protocol, that is effificient in terms of computation and communication, and resilient to client dropouts.&lt;/p&gt;
novel concept specific deep learning for disease treatment prediction on patient trajectory data model

Prediction of diseases by researchers in healthcare industries and the different machine learning algorithms involved play an important role in disease prediction. Similarly, the prediction of patient trajectory data and its analysis also plays a vital role. A novel deep learning approach named Deep Concept Specific learning has been proposed for analyzing unstructured patient trajectory data. The proposed model is structured as a generic model suitable for any patient's medical disease. To evaluate the proposed model performance, the disease related to diabetes has been employed. It implies for autoencoder model, which has been optimized with hyper parameter tuning. It effectively transforms high dimensional features into low dimensional feature space to extract concept-specific features. It is implemented on the Further, all the parameters employed in the sparse features are manipulated. They are considered a loss and encoder function to compute the optimal features for effectively predicting the results. The encoder function maps the data points containing optimal features into latent representations based on disease characteristics. It is done from the patients' perspectives by the ReLu activation function based on generated feature characteristics.
The Role of Teachers in the Application of Digital Literacy to Create a Digital Literate Generation

Entering the era of the industrial revolution 4.0, especially the world of education, schools, teachers and students live in an advanced digital world. In this regard, the existence and role of teachers is very important for digital literacy. In today's conditions, the role of the teacher is very necessary, as it is known that the content on the internet is almost unlimited with a variety of information with an abundance of volume, while teachers and parents cannot always control internet access by students at all times. This study aims to answer the question of how digital literacy is implemented in schools and what are the obstacles faced by teachers in teaching digital literacy. This is a qualitative research with a phenomenological approach. Data collection was obtained from in-depth interviews with 20 teachers. This research shows that there are still many teachers who have not had the opportunity to take part in training related to digital literacy, both organised by the government and the private sector. Therefore, teachers are expected to be proactive in seeking information related to digital literacy from various sources. This study also proves that socialisation related to digital literacy is still very lacking. The government and related parties must be more intensive in conducting socialisation so that the community, especially parents, also understands the importance of digital literacy.
ANALYSIS OF EDITORIAL TEXT TEACHING MATERIALS TO IMPROVE STUDENTS' WRITING SKILLS

This study is to analyze the editorial text teaching materials used by teachers in schools. In addition, to determine the ability of students in compiling editorial texts. The research method used is descriptive qualitative. The research subjects were 5 teachers as experts and users of teaching materials in textbooks. There were 98 students as respondents to find out the ability to write editorial texts and the benefits of using textbooks as a learning guide. Based on the results of the study, it was explained that 80% of teachers use textbooks in each lesson because they greatly help students' understanding. The use of reading texts in textbooks 60% of teachers said they were less contextual or in accordance with the needs of students. The order of the percentage of input from the teacher towards editorial text teaching materials in the textbook is 80% in the presentation, 80% of the content of the material, 60% of the reading text, 40% of the design and 20% of the mechanical elements. The results of the research from students stated that editorial text teaching materials helped student learning. The presentation of teaching materials from textbooks greatly affects students' understanding and ability to write editorial texts. From the results of the students' editorial text writing, 20% were able to compose it well, 30% of students could compile a text only needed to be guided first by the teacher, and 50% of students had not been able to compile an editorial text because they were confused with the chosen idea and the development of the writing.
English Language Apprehension of Education Student: A Phenomenology

Language Anxiety is acknowledged as an impediment in second language learning. In addressing the issue, this study aimed to explore the English language Apprehension among Education Students. This study utilized qualitative design using a phenomenological method involving one focus group discussion with 10 participants and in-depth interviews with eight informants from the College of Teacher Education at the University of Mindanao. The results of this study reveal that the apprehensions experienced by the participants are caused by being measured against a standard, being a laughingstock or a sorry spectacle, being unable to deliver the required performance, and dealing with their insecurities and self-doubt. In coping with the challenges, the participants engage in activities that provide needed exposure, reinforcement through reading and listening to exercises, and being open and positive. Also, they resort to withdrawal and disengagement. The implications of this study must be a well-established intervention program; also, students should improve their skills through constant practice and exposure to the language.
Cybernetics and Physics: ten years online together

Prehistory and history of the Cybernetics and Physics journal are outlined on occasion of its ten years jubilee. Recent trends in the scope of the journal are discussed.
$M_W$ helps select $Z^\prime$ models for $b \rightarrow s \ell \ell$ anomalies

As shown in Ref. \cite{Allanach:2021kzj}, the Third Family Hypercharge ($Y_3$) Model changes the Standard Model prediction for $M_W$ whilst simultaneously explaining anomalies in $b\to s\ell\ell$ transitions via a heavy $Z^\prime$ gauge boson which is spawned by a spontaneously broken gauged $U(1)_{Y_3}$ symmetry. The 2022 CDF II measurement of $M_W$, which is far from the Standard Model prediction, somewhat disfavours the $Y_3$ model. Here, we generalise the gauge charge assignments to the anomaly-free combination $s Y_3 + t (B_3-L_3)$ and show that incorporating the 2022 CDF II measurement of $M_W$ selects a viable domain of integers $s$ and $t$. For example, $s=1, t=-3$ yields a $p-$value of .12 in a two-parameter global fit to 277 electroweak and flavour changing $b$ data, much improving a SM $p-$value of $5\times 10^{-6}$.
K-Nearest Neighbors Algorithm to Student Opinion of the Online Learning Method at Wira Wacana Sumba Christian University

The education sector is one of the areas that has felt the major impact of the Covid-19 pandemic. The impact that arises is the teaching and learning process must be carried out from home using the online learning method. This teaching and learning method raises a variety of responses from students. This is what makes researchers analyze these views, both in the form of positive opinions or negative opinions. The analysis process is carried out by applying sentiment analysis or opinion mining from the comment on Facebook, text mining is processed using the preprocessing method, labeled it to positive and negative. Based on the available data, a classification process is carried out using the K-Nearest Neighbors algorithm. Rapid Miner is used to experiment text data with the KNN algorithm in order to find the value of accuracy, precision and recall. From the results of research, it was obtained a value of 87.00% for accuracy and 0.916 for the AUC value. The values ​​are high enough for the classification of student opinion against this pandemic so that this research is classified as Excellent Classification.
Different Markov chains modulate visual stimuli processing in a Go-Go experiment in 2D, 3D and augmented reality

Abstract The introduction of Augmented Reality (AR) has atracted several developments, although the people’s experience of AR has not been clearly studied or contrasted with the human experience to 2D and 3D environments. Here, the directional task was applied in 2D, 3D and AR using simplified stimulus in video games to determine whether there is a difference in human answer reaction time prediction using context stimulus. Testing of the directional task adapted task paradigm is in the Supplementary MaterialResearch question: Are the main differences between 2D, 3D and AR able to be predicted using Markov chains? Methods : A computer was fitted with a digital acquisition card in order to record, test and validate the reaction time (RT) of participants (see Supplementary Material) attached to the arranged RT for the theory of Markov chain probability. A prior survey was conducted and served as a pre-assessment in order to better interpret the data collected from the user throughout the experiment. A Markov chain analysis was performed on the participants’ data. Subsequently, the way certain factors influenced participants RT amongs the three tasks time on the accuracy of the participants was sought in the three tasks (environments) were statististically tested using ANOVA. Results : Markov chains of order 1 and 2 successfully reproduced the average reaction time by participants in 3D and AR tasks, having only 2D task with the variance predicted with the current state. Moreover, a clear explanation of delayed reaction time at every modality was done and quantified.Our results suggest that unconscious preparation of selective choices is not restricted to current motor preparation. Instead, decisions at different environment evolve from the dynamics of preceding cognitive activity.
Exploring Cross-lingual Textual Style Transfer with Large Multilingual Language Models

Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text. Existing detoxification methods are designed to work in one exact language. This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models like in this setting. Unlike previous works we aim to make large language models able to perform detoxification without direct fine-tuning in given language. Experiments show that multilingual models are capable of performing multilingual style transfer. However, models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is inevitable.
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts

Forecasting tasks surrounding the dynamics of low-level human behavior are of significance to multiple research domains. In such settings, methods for explaining specific forecasts can enable domain experts to gain insights into the predictive relationships between behaviors. In this work, we introduce and address the following question: given a probabilistic forecasting model how can we identify observed windows that the model considers salient when making its forecasts? We build upon a general definition of information-theoretic saliency grounded in human perception and extend it to forecasting settings by leveraging a crucial attribute of the domain: a single observation can result in multiple valid futures. We propose to express the saliency of an observed window in terms of the differential entropy of the resulting predicted future distribution. In contrast to existing methods that either require explicit training of the saliency mechanism or access to the internal states of the forecasting model, we obtain a closed-form solution for the saliency map for commonly used density functions in probabilistic forecasting. We empirically demonstrate how our framework can recover salient observed windows from head pose features for the sample task of speaking-turn forecasting using a synthesized conversation dataset.
Penerapan Model Pembelajaran Inkuiri Bebas Upaya Meningkatkan Prestasi Belajar Matematika Dasar Menggunakan Aplikasi Microsoft Excel 2010

This research is conducted based on observation that learning about mathematics in the class is always use convensional method. This research is sopposed to know learning achievement in the free Inquiry learning model with Microsoft Excel 2010 application student SMA Muhammadiyah 1 Bengkulu. The methodology that is use classroom action research, 1. The application of the free inquiry learning model using the Microsoft Excel 2010 application can improve student achievement in mathematics, 1. The application of the free inquiry learning model using the Microsoft Excel 2010 application can improve student and effective in improving student achievement. The results obtained from the pre-test to the post-test in each cycle from the t-test results in each cycle showed that there was a significant difference in student achievement after the application of the free inquiry learning model using the Microsoft Excel 2010 application.&#x0D;
Developing a Quality Automation Framework to Assess Specifications for Academic Accreditation in Saudi Arabian Universities

Saudi Arabia's higher educational standards are gradually evolving to match the country's vision 2030. The importance of education and a strong emphasis on educational quality are the determinants to realize this vision. Recent studies reveal that the faculty members spend a lot of time preparing documents for the accreditation program and eventually may lose interest in the process. There is a lack of an automated system to prepare documents for accreditation and the need for one is strongly felt. Educational institutions entail multiple accreditations to ensure their quality. In addition, accreditation enables educational institutions to attract student communities. The study intends to develop an integrated framework to prepare documents and maintain quality practices for multiple academic accreditations. The proposed system employs application-programming interfaces for publishing data from the database that facilitates an added security to the primary data. Moreover, it automates the overall accreditation process and minimizes manual interaction. Researchers employed the database of the AlMaarefa University and used it as a testbed for implementing the framework.The initial set of results are promising and support educational institutions to improve their quality of service.
Multi-Game Decision Transformers

A longstanding goal of the field of AI is a method for learning a highly capable, generalist agent from diverse experience. In the subfields of vision and language, this was largely achieved by scaling up transformer-based models and training them on large, diverse datasets. Motivated by this progress, we investigate whether the same strategy can be used to produce generalist reinforcement learning agents. Specifically, we show that a single transformer-based model - with a single set of weights - trained purely offline can play a suite of up to 46 Atari games simultaneously at close-to-human performance. When trained and evaluated appropriately, we find that the same trends observed in language and vision hold, including scaling of performance with model size and rapid adaptation to new games via fine-tuning. We compare several approaches in this multi-game setting, such as online and offline RL methods and behavioral cloning, and find that our Multi-Game Decision Transformer models offer the best scalability and performance. We release the pre-trained models and code to encourage further research in this direction.
Learning to generate imaginary tasks for improving generalization in meta-learning

The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks. Frequent violation of the assumption in applications with either insufficient tasks or a very narrow meta-training task distribution leads to memorization or learner overfitting. Recent solutions have pursued augmentation of meta-training tasks, while it is still an open question to generate both correct and sufficiently imaginary tasks. In this paper, we seek an approach that up-samples meta-training tasks from the task representation via a task up-sampling network. Besides, the resulting approach named Adversarial Task Up-sampling (ATU) suffices to generate tasks that can maximally contribute to the latest meta-learner by maximizing an adversarial loss. On few-shot sine regression and image classification datasets, we empirically validate the marked improvement of ATU over state-of-the-art task augmentation strategies in the meta-testing performance and also the quality of up-sampled tasks.