Reviewerly/exHarmony huggingface dataset:
El escape room como estrategia didáctica en el Máster de Profesorado

espanolAnte la falta de motivacion del alumnado universitario, se hacen necesarias metodologias activas que les brinden un rol protagonista dentro del proceso de ensenanza-aprendizaje. Dentro de ellas encontramos el aprendizaje basado en juegos, destacando el enorme auge en los ultimos anos de los escape rooms en los procesos formativos. En este articulo se pretende analizar lo que este tipo de planteamientos puede generar en el contexto universitario, a traves de las percepciones y valoraciones del alumnado del master de profesorado, tras participar en un escape room digital basado en la pelicula Matrix. La metodologia utilizada fue mixta, utilizando la escala GAMEX para obtener datos cuantitativos sobre cada una de las dimensiones que esta mide, relacionadas con la participacion en experiencias gamificadas. Esta informacion se complemento con un cuestionario con una unica pregunta abierta para que pudieran compartir sus opiniones y experiencias, haciendo un procesamiento cualitativo de dicha informacion. Los resultados muestran valores muy positivos en el disfrute/diversion, el grado de absorcion, el pensamiento creativo y dominio y la activacion del alumnado. Ademas, el planteamiento potencio la gestion emocional, fundamental en los futuros docentes, pues tambien aparecen sentimientos como la frustracion o la molestia. El hecho de vivir estas experiencias en primera persona permite a los estudiantes conocer su potencial y dificultades de cara a poder aplicarlas en un futuro en las aulas. EnglishWhen facing the lack of motivation of university students, it is necessary to use active methodologies which will provide the students with an active role. Among them, we find game-based learning, we highlight the huge peak in the last few years of escape rooms among the formative processes. In this article, we analyse what can be generated in the university context with this type of approach, through the perceptions and reviews of the student body from the MA in Teaching after they take part in a digital escape room based on the movie Matrix. The employed methodology is a mixed one, using the GAMEX scale to obtain quantitative data about every single one of the dimensions measured by the scale that are relates to the participation in gamified experiences. This information was complemented with an open-question based questionnaire so the students could share their opinions and experiences, making use of qualitative way of processing this information. The results show very positive values in the enjoyment/fun, grade of absorption, creative thinking and dominion and the student body’s activation. Furthermore, this approach boosted emotional management, something fundamental in future teachers since they also feel frustration or annoyance. The fact they lived this experience directly allows the students to know their potential and their difficulties so they can apply them in the future.
AN ANALYSIS OF TEACHERS AND STUDENTS' PERCEPTION OF ONLINE READING AT INTEGRATED ISLAMIC ELEMENTARY SCHOOL

This research purposes to analyze, how the teachers’ perceptions and students' perceptions of online reading in English learning. The explanation in this research is about the elements that influence online reading, the benefits of using online reading in class, and how online reading may help in the learning process, especially in reading. The questionnaire consists of fifteen statements and each statement has four alternative responses from one to four: agree, strongly agree, disagree, and strongly disagree. The questionnaire was distributed using Google Forms, and then the respondents were interviewed using WhatsApp. The results of the research indicate that teachers and the students show positive responses towards online reading. The teachers and the students perceived that online reading is ease than conventional reading. According to the research, there are benefits, including the ability for the teachers and students are to quickly find a reading topic by searching keywords, the ability for online reading to encourage students' independent learning attitudes, and students' increased interest in technology-based learning. There is one disadvantage: online reading is to be limited by the devices and tools accessible.
A Synthesis of Human and Machine: Correlating “New” Automatic Evaluation Metrics with Human Assessments

The session will provide an overview of some of the new Machine Translation metrics available on the market, analyze if and how these new metrics correlate at a segment level to the results of Adequacy and Fluency Human Assessments, and how they compare against TER scores and Levenshtein Distance – two of our currently preferred metrics – as well as against each of the other. The information in this session will help to get a better understanding of their strengths and weaknesses and make informed decisions when it comes to forecasting MT production.
Comparative Studies on Typical Opposition-Based Learning Schemes in Differential Evolution with Gaussian Perturbation

Some optimization problems have been well solved after integrating the concept of opposition-based learning (OBL), and opposition-based differential evolution (ODE) is a typical representative. ODE can be seen as a rigorous point-to-point algorithm, which may lead to neglect of some good solutions in the neighborhood of the opposite point. It provides a relatively narrow search area for candidate solutions and is not enough to maintain population diversity. Gaussian perturbation strategy is proposed to implement around the opposite point to expand the search neighborhood of the opposite point and to find more and better solutions. A stochastic centroid opposition (SCO) is firstly proposed on the basis of centroid opposition (CO) in this paper. Then four typical OBL schemes, namely, the original OBL, generalized OBL, centroid opposition and stochastic centroid opposition, are embedded in differential evolution (DE) with Gaussian perturbation strategy. Comparison experiments based on CEC 2014 benchmark suite are reported and the results indicate that the opposition algorithms with Gaussian perturbation strategy based on individuals’ dynamic boundaries perform better than the corresponding centroid algorithms.
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation

Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to generate informative sentences for clinical diagnoses since normal findings dominate the datasets. To tackle this challenge and encourage more clinically-accurate text outputs, we propose a novel weakly supervised contrastive loss for medical report generation. Experimental results demonstrate that our method benefits from contrasting target reports with incorrect but semantically-close ones. It outperforms previous work on both clinical correctness and text generation metrics for two public benchmarks.
COLLABORATION OF CA COUNTRIES FOR DEVELOPING INNOVATIVE TEACHING PROGRAMS AND THEIR IMPLEMENTATION TO ADULT’S EDUCATION

This paper discusses the issues of joint development and implementation of modern methods of Intelligent Big Data Analysis in the context of the developing digital economy and innovative education in the CA countries. The necessity of developing such innovative methods in the light of existing problems and their applicability for adult education is substantiated. Their special relevance for analyzing the problems of the current situation with the spread of the coronavirus of the COVID-19 is emphasized.
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms

Zeroth-order (ZO) optimization is widely used to handle challenging tasks, such as query-based black-box adversarial attacks and reinforcement learning. Various attempts have been made to integrate prior information into the gradient estimation procedure based on finite differences, with promising empirical results. However, their convergence properties are not well understood. This paper makes an attempt to fill this gap by analyzing the convergence of prior-guided ZO algorithms under a greedy descent framework with various gradient estimators. We provide a convergence guarantee for the prior-guided random gradient-free (PRGF) algorithms. Moreover, to further accelerate over greedy descent methods, we present a new accelerated random search (ARS) algorithm that incorporates prior information, together with a convergence analysis. Finally, our theoretical results are confirmed by experiments on several numerical benchmarks as well as adversarial attacks.
Comment on ‘Parameter-dependent unitary transformation approach for quantum Rabi model’

We discuss the elementary errors in D.G. Zhang's [New. J. Phys. 23, 093014 (2021)] claimed exact solution of the quantum Rabi model. The erroneous solution is seen to be nothing more than the combined solution of the simpler Jaynes-Cummings and anti-Jaynes-Cummings models obtained by neglecting terms in the model Hamiltonian.
Cryptocurrencies and finance theories / Abdul Quddoos Abdul Basith, Mohammed M Elgammal and Bana Abuzayed

Cryptocurrency (CCY) as a new key player in the currency system that has drawn the attention of scholars to examine its influence, relations and the opportunities that it may provide. However, a financial theoretical framework to connect CCY with financial theory is missing. This paper fills this gap by providing a review for the theoretical framework introduced in the literature to position CCY in investment and finance theories. This is done by studying the CCY literature and providing a critical feedback on the overall contributions in the area and possible venues for improvement. We report a need for a long-term analysis for CCY as this asset class is fairly new and sufficient data may not be available. Moreover, a better connection and linking with finance theories is required as it is significantly deficient. The promising potential of block chain/ CCY stresses the need for interdisciplinary research including business, legal and information technology disciplines. In addition, the Covid-19 pandemic opens the door for further research to investigate the role of CCY as a hedge in the times of crises.
DONALD TRUMP’S IMPEACHMENT VIEW ON JAKARTA POST: A CRITICAL DISCOURSE ANALYSIS

This thesis investigates the discourse elements in editorial news from The Jakarta Post about the impeachment of Donald Trump. The writer presents Van Dijk critical discourse analysis concept on the text analysis structure, social cognition and social context. The aims of this research revealed the discursive strategy, the ideology represented on social cognition and how the society acknowledge the discourse on social context of Donald Trump impeachment in Jakarta Post editorial news. From the data and concept above, the writer used qualitative method by using descriptive and analysis technique. The discursive strategy from the editorial news is identified and analyze from microstructure, macrostructure and also superstructure. Then, the social cognition analysis is identified the ideology represent using ideological analysis with the principle ideological square by Van Dijk. Also, for the social context analysis, this research describes public survey regarding Donald Trump along with his impeachment and political figure statements to look how the discourse acknowledge in the society. As the results, the discursive strategy from Jakarta Post to describe Trump impeachment using text analysis elements such as thematic, schematic, semantic, syntax, stylistic, and also rhetoric. For social cognition analysis, the editorial represents Donald Trump in emphasize negative things and de-emphasize positive things. Lastly, the society in the public survey acknowledge Trump deserve for the removal from office, done illegal and unethical things.
Development Of AI Chatbot To Learn Programming

With advancements made in the field of Artificial Intelligence, development of AI- powered chatbots that possess communication capabilities just like humans have been developed. Chatbots are based on techniques like Natural Language Processing that can understand the queries put forth by users and respond with them appropriately via a User Interface. They possess the ability to analyze data more accurately, thereby providing the right information to the user. This paper deals with the development of AI Chatbot using RASA which helps users learn about programming. RASA is a tool to develop and build custom AI chatbots with the help of Natural Language Understanding (NLU).
Heuristic-Guided Reinforcement Learning

We provide a framework for accelerating reinforcement learning (RL) algorithms by heuristics constructed from domain knowledge or offline data. Tabula rasa RL algorithms require environment interactions or computation that scales with the horizon of the sequential decision-making task. Using our framework, we show how heuristic-guided RL induces a much shorter-horizon subproblem that provably solves the original task. Our framework can be viewed as a horizon-based regularization for controlling bias and variance in RL under a finite interaction budget. On the theoretical side, we characterize properties of a good and its impact on RL acceleration. In particular, we introduce the novel concept of an improvable heuristic -- a that allows an RL agent to extrapolate beyond its prior knowledge. On the empirical side, we instantiate our framework to accelerate several state-of-the-art algorithms in simulated robotic control tasks and procedurally generated games. Our framework complements the rich literature on warm-starting RL with expert demonstrations or exploratory datasets, and introduces a principled method for injecting prior knowledge into RL.
SIGNATURE BASED COMPUTERIZED CRIMINAL SYSTEM USING HYBRID TECHNIQUES.

The complexity and anonymity of computer systems may help criminal camouflage their operations. This paper identifies the challenges facing police departments that seek to implement computerized criminal detection systems. . This system helps identify potential suspects to increase investigators suspect base when no leads are evident. The ability to access and process information quickly while displaying it in a spatial and visual medium allows agencies to allocate resources quickly and more effectively. The goal of this research is to enable the police to layer the data and view the data most critical to the particular issue or mission. Hence the new system designed will aid police departments, both large and small, to provide solutions for crime analysis, criminal tracking, traffic safety, community policing, Intranet/Internet mapping, and numerous other tasks.
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization

(Partial) ranking loss is a commonly used evaluation measure for multi-label classification, which is usually optimized with convex surrogates for computational efficiency. Prior theoretical work on multi-label ranking mainly focuses on (Fisher) consistency analyses. However, there is a gap between existing theory and practice -- some pairwise losses can lead to promising performance but lack consistency, while some univariate losses are consistent but usually have no clear superiority in practice. In this paper, we attempt to fill this gap through a systematic study from two complementary perspectives of consistency and generalization error bounds of learning algorithms. Our results show that learning algorithms with the consistent univariate loss have an error bound of $O(c)$ ($c$ is the number of labels), while algorithms with the inconsistent pairwise loss depend on $O(\sqrt{c})$ as shown in prior work. This explains that the latter can achieve better performance than the former in practice. Moreover, we present an inconsistent reweighted univariate loss-based learning algorithm that enjoys an error bound of $O(\sqrt{c})$ for promising performance as well as the computational efficiency of univariate losses. Finally, experimental results validate our theoretical analyses.
Integrating Agriculture-related Data Provided by Thematic Networks into a High Impact Knowledge Reservoir

Data representation is an issue of increased importance when needing to consider heterogeneous data coming from a range of sources. The challenges related to the efficient storage and management of data of a variety of formats and types are at the core of the EURAKNOS project. More specifically, EURAKNOS aims to collate and allow access to data related to the agricultural production in Europe that is made available by Thematic Networks. In order to attain its goal and make contributions towards the direction of efficiently handling data diversity and heterogeneity, EURAKNOS has undertaken the task of developing an ontology for a unified and homogeneous, formal description of the data disseminated by Thematic Networks. The present book chapter focuses on the presentation of the process followed for the ontology development and the result obtained. In order to frame the context of the work in EURAKNOS, the concept of Thematic Networks is explained and the methodology adopted is detailed. The description of the conceptual modeling – related work is followed by a discussion on the EURAKNOS platform and its architecture. The book chapter concludes by underlining the project’s contributions and limitations, as well as by listing a number of steps to be further taken. The project’s potential impact for the future Thematic Networks and policy implications is also discussed.
The Aplication of TAI Learning Model Based on PBI to Increase Activity and Mathematics Learnings Outcomes

AbstrackThis study aims to improve student activity and learning outcomes through the TAI learning model based on PBI. This type of research is Classroom Action Research (CAR). The research subjects were students of class VIII SMP An-Nisa Gumukmas. The data collection methods used were interview, observation, test and documentation methods. This research was conducted in two cycles. Based on the results of the research that has been done, the percentage of activity in the first cycle is 56.71% and in the second cycle it is 71.99%, that there appears to be an increase. Then for the average value in cycle I is 68.89 with a percentage of learning outcomes completeness of 55.56% and in cycle II an average value of 78.67 is obtained with a percentage of 92.59%, this shows that there appears to be an increase in activity and student learning outcomes by applying the TAI learning model based on PBI. From the results obtained, it can be concluded that the application of the TAI learning model based on PBI can increase student activity and learning outcomes.Keywords: TAI, PBI, learning activity, learning outcomes
Rethinking Sentiment Style Transfer.

Though remarkable efforts have been made in non-parallel text style transfer, the evaluation system is unsatisfactory. It always evaluates over samples from only one checkpoint of the model and compares three metrics, i.e., transfer accuracy, BLEU score, and PPL score. In this paper, we argue the inappropriateness of both existing evaluation metrics and the evaluation method. Specifically, for evaluation metrics, we make a detailed analysis and comparison from three aspects: style transfer, content preservation, and naturalness; for the evaluation method, we reiterate the fallacy of picking one checkpoint for model comparison. As a result, we establish a robust evaluation method by examining the trade-off between style transfer and naturalness, and between content preservation and naturalness. Notably, we elaborate the human evaluation and automatically identify the inaccurate measurement of content preservation computed by the BLEU score. To overcome this issue, we propose a graph-based method to extract attribute content and attribute-independent content from input sentences in the YELP dataset and IMDB dataset. With the modified datasets, we design a new evaluation metric called “attribute hit” and propose an efficient regularization to leverage the attribute-dependent content and attribute-independent content as guiding signals. Experimental results have demonstrated the effectiveness of the proposed strategy.
Multi-Modal Open-Domain Dialogue

Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020; Roller et al., 2020). However, if we want to build agents with human-like abilities, we must expand beyond handling just text. A particularly important topic is the ability to see images and communicate about what is perceived. With the goal of getting humans to engage in multi-modal dialogue, we investigate combining components from state-of-the-art open-domain dialogue agents with those from state-of-the-art vision models. We study incorporating different image fusion schemes and domain-adaptive pre-training and fine-tuning strategies, and show that our best resulting model outperforms strong existing models in multi-modal dialogue while simultaneously performing as well as its predecessor (text-only) BlenderBot (Roller et al., 2020) in text-based conversation. We additionally investigate and incorporate safety components in our final model, and show that such efforts do not diminish model performance with respect to human preference.
Earthquake Information System

IAEMIS (Integrated Automated Emergency Management Information System) is the principal tool of an earthquake preparedness program developed by Martin Marietta and the Mid-America Remote Sensing Center (MARC). It is a two-component set of software, data and procedures to provide information enabling management personnel to make informed decisions in disaster situations. The NASA-developed program ELAS, originally used to analyze Landsat data, provides MARC with a spatially-oriented information management system. Additional MARC projects include land resources management, and development of socioeconomic data.
A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce

Abstract: Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis