Reviewerly/exHarmony huggingface dataset:
Pedagogical device: training of teaching practices in university education

&#x0D; &#x0D; &#x0D; A review of the pedagogical device is carried out: teaching practice in the teaching-learning process of the student of the Faculty of Philosophy, Letters and Education Sciences, at the University of Guayaquil with the following categories of analysis: collaborative learning. training, training extensions, organization of remote connectivity, project-based learning, research processes, classroom projects, case studies, didactic guides, training research. The research methodology is descriptive with a bibliographic design having a direct observation and with a survey of 164 students of the career of Pedagogy of Mathematical Physics, Pedagogy of History and Social Sciences in addition to the Career of Pedagogy of Chemistry and Science. Biology, which indicated that the best activation of teacher training is through classroom projects with active techniques such as project-based learning, study guides and case studies. The main objective is to identify the pedagogical devices of teaching practice in university education for the Faculty of Philosophy, Letters and Education Sciences. In conclusion, it is necessary to specify proactive and defined theoretical positions to build the development of the training of teaching practices in collaborative work at the University of Guayaquil.&#x0D; &#x0D; &#x0D;
Modeling Entity Knowledge for Fact Verification

Fact verification is a challenging task of identifying the truthfulness of given claims based on the retrieval of relevant evidence texts. Many claims require understanding and reasoning over external entity information for precise verification. In this paper, we propose a novel fact verification model using entity knowledge to enhance its performance. We retrieve descriptive text from Wikipedia for each entity, and then encode these descriptions by a smaller lightweight network to be fed into the main verification model. Furthermore, we boost model performance by adopting and predicting the relatedness between the claim and each evidence as additional signals. We demonstrate experimentally on a large-scale benchmark dataset FEVER that our framework achieves competitive results with a FEVER score of 72.89% on the test set.
On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling

Although recent developments in neural architectures and pre-trained representations have greatly increased state-of-the-art model performance on fully-supervised semantic role labeling (SRL), the task remains challenging for languages where supervised SRL training data are not abundant.Cross-lingual learning can improve performance in this setting by transferring knowledge from high-resource languages to low-resource ones.Moreover, we hypothesize that annotations of syntactic dependencies can be leveraged to further facilitate cross-lingual transfer.In this work, we perform an empirical exploration of the helpfulness of syntactic supervision for crosslingual SRL within a simple multitask learning scheme.With comprehensive evaluations across ten languages (in addition to English) and three SRL benchmark datasets, including both dependency-and span-based SRL, we show the effectiveness of syntactic supervision in low-resource scenarios.Experiments Target Languages SRL Style Same Frames?Compatible Roles?Main SRL Setting EWT/UPB † ( §3.2) de,fr,it,es,pt,fi Dependency-based Yes
A new corresponding color dataset covering a wide luminance range under high dynamic range viewing condition

An experiment was carried out to investigate the change of color appearance for 13 surface stimuli viewed under a wide range of illuminance levels (15-32000 lux) using asymmetrical matching method.Addition to the above, in the visual field, observers viewed colours in a dark (10 lux) and a bright (200000 lux) illuminance level at the same time to simulate HDR viewing condition.The results were used to understand the relationship between the color changes under HDR conditions, to generate a corresponding color dataset and to verify color appearance model, such as CIECAM16.
Implementasi Teknologi Augmented Reality Menggunakan Magic Book Sebagai Media Pemasaran Produk Berbasis Android

This study is conducted based on the problem faced by the owner of the Nurhayati Shop. motivated by the obstacles that are often experienced by the owner nurhayati shop. The 2D Tupperware catalog used by Nurhayati Shop becomes the core problem in serving the customers due to the inefficient time and product description. This study applies Multimedia Life Cycle (MDLC) as the method. The result shows that augmented reality technology integrated into a magic book can be used as the marketing media with android-base. Technology helps the seller in promoting the product throught the android that changes the object into 3D. Besides, it helps the buyer to have an effective time to choose the product.
Comparing Supervised Machine Learning Techniques for Genre Analysis in Software Engineering Research Articles

Written communication is of utmost importance to the progress of scientific research.The speed of such development, however, may be affected by the scarcity of reviewers to referee the quality of research articles.In this context, automatic approaches that are able to query linguistic segments in written contributions by detecting the presence or absence of common rhetorical patterns have become a necessity in the refereeing process.This paper aims to compare supervised machine learning techniques tested to accomplish genre analysis in Introduction sections of software engineering articles.A semi-supervised approach to augment the number of annotated sentences in SciSents 1 was performed.Two supervised approaches using SVM and logistic regression to assess the F-score for genre analysis in the corpus were undertaken.A technique based on logistic regression and BERT has been found to perform genre analysis highly satisfactorily with an average of 88.25 on F-score when retrieving patterns at an overall level.
기계학습의 활용에서 발생하는 편향과 차별: 학습자료의 활용

The use of artificial intelligence is expanding to various fields of society as Korea enters the 4th industrial revolution. Artificial intelligence based on machine learning causes problems such as bias and discrimination due to biased data along with the positive aspects of improving national and individual productivity and quality of life in society and economy. Therefore, in this study, the level of errors in prediction and classification that may occur by using biased learning data was verified. Specifically, this study verified the error of prediction that is expected to be obtained using artificial intelligence by applying the biased algorithm to actual data. The main results are as follows. First, prediction level decreased when the attributes of the population represented by the learning data and the actual data were different. Second, errors in prediction and classification increased when the learning data contained predictors that had a biased influence on the criterion variable. This study emphasized the importance of unbiased learning data to properly apply the algorithm using artificial intelligence.
Analysis of Approaches of Trustability and Issues in Cloud Computing

Cloud computing is a technology still used today. The cloud is used by a large number of people and can belong to any organization. There are many issues, such as security, trust, and reliability. To help end-users and protect them from various attacks, there is a need to analyze the existing Trustability methods. The word "Trustability" comes from two factors: trust and reliability. In a cloud environment, one needs to design a framework that calculates Trustability. But first, there is a need to analyze and apply current Trustability methods. Trustability factor that determines how reliable a device is for a given attack. The trust management system provides support to cloud administrators and customers. This article analyzes the current problems of cloud computing and applies the Trustability method.
Simulasi Tata Cara Pengurusan Jenazah Berbasis Multimedia Dalam Bentuk Animasi 3d

Funeral management that has been determined in Islam is fardhu kifayah which means an obligation for all Muslims. Funeral management can be done by anyone as long as they follow the guidance of Islam. There are cases of the dead being taken care of by non-family member, due to not understanding religious knowledge and unable to organize it. Management carried out by the family ensures the dead’s secret safety and the family gets religious reward because it’s their right. Currently teaching how to do funeral management has started using multimedia. However, the presence of the presenter must exist regardless of the media used, so that knowledge can be absorbed effectively and learning experience can be applied correctly. This research resulted in a 3D animation interactive website to help learn the funeral management through multimedia. Three test was performed. In expert validation testing, the animation already displays the correct information. Then the feedback test, 89.1% of 30 respondents stated that the interactive 3D animation website on corpse management procedures helps users understand the management of corpses and prepares respondents to practice corpse management. Finally, black box testing, all features run as they function.
LibGuides: Intro to Database Research for Elementary Students: SIRS Discoverer

An overview of digital search techniques and an introduction to pertinent databases for upper elementary students
Evaluating Recognizing Question Entailment Methods for a Portuguese Community Question-Answering System about Diabetes Mellitus

This study describes the development of a Portuguese Community-Question Answering benchmark in the domain of Diabetes Mellitus using a Recognizing Question Entailment (RQE) approach.Given a premise question, RQE aims to retrieve semantically similar, already answered, archived questions.We build a new Portuguese benchmark corpus with 785 pairs between premise questions and archived answered questions marked with relevance judgments by medical experts.Based on the benchmark corpus, we leveraged and evaluated several RQE approaches ranging from traditional information retrieval methods to novel large pre-trained language models and ensemble techniques using learn-to-rank approaches.Our experimental results show that a supervised transformer-based method trained with multiple languages and for multiple tasks (MUSE) outperforms the alternatives.Our results also show that ensembles of methods (stacking) as well as a traditional (light) information retrieval method (BM25) can produce competitive results.Finally, among the tested strategies, those that exploit only the question (not the answer), provide the best effectiveness-efficiency trade-off.Code is publicly available 1 .
Automation in Applications for Healthcare, Private, and Public Sectors in IT

The paper will examine the implementation of the new technology in the automation of the healthcare, private, and public sectors. The latest technological advancements tend to alter the modes of the normal behavior of different segments of life among the listed ones in their entire process and responsibly affect their operation. The technology sheds light on specific areas requiring adequate and superb services and offers obscure methods in the field of automation. In doing so, the credibility and accountability of the entire systems that are put in place are enhanced. To investigate how the automation of the application areas is influenced by IT regarding the equation of accountability, a range of documentations that are empirically implemented in the field of IT are explored. The exploration ranges from the purest forms of transactions that involve the automation of lower levels to the highly automated systems, which includes the technologies that analyze biometric fingerprints, among others. From the example, the accountable potential of the IT automation is discussed for the different applications with the hope of investigating the benefits that are brought about by the automation of the applications while doing away with the potentials that are unaccountable and hinders the functionality that may come from the usage of the systems applications on the different fields. Throughout the paper, much stress is on the importance of developing a balance that exists between the benefits of the automated applications in IT and the entire process of automation, including the system that may tend to reduce the efficiency of the applications and be able to bring up accountability.
Human Elements in Machine Learning-Based Solutions to Cybersecurity

Cybersecurity has become one of the key challenges in every aspect of computing. Increasingly, machine learning (ML) approaches are used in various areas of cybersecurity such as phishing detection, malware detection, and intrusion detection. ML algorithms transform data into computational models that drive cybersecurity applications. In this process, three types of human actors are involved: ML practitioners, domain experts, and end-users. In traditional ML workflow, domain experts label data. ML practitioners extract features, experiment with different algorithms, tune parameters, train, and evaluate models. If the model is deployed, end-users knowingly or unknowingly are at the receiving ends of the solutions. An incorrect decision made by an ML model could result in a successful cyberattack, or if a model produces many false positives, the model will not be trusted by its users. As a result, an ML-based model needs to leverage human expertise, as well as humans need to understand the reasoning behind the decision made by the model. Therefore, ML workflow needs to tightly integrate humans in its loop. We survey the roles of human elements in both traditional and interactive ML-based solutions to cybersecurity. This chapter also describes the interactive ML paradigm where humans iteratively provide feedback in response to the output of the model.
On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption

We develop a theory that shows signaling a firm’s fundamental quality (e.g., its operational capabilities) to lenders through inventory transactions to be more efficient—it leads to less costly ope...
Perception of Hate Speech on Social Media among Residents of Keffi and Lafia Local Government Areas of Nasarawa State

This study was carried out to examine social media and its effects on hate speech in Lafia and Keffi. Survey research design was used, while questionnaire was adopted as the instrument of data collection. Simple percentages and ANOVA statistics were used for data analysis and interpretation. Findings revealed that there was widespread hate speech in the social media space, most especially on Facebook, thereby spurring violence, ethnic and religious hatred, among other discords. The findings also revealed that social media platforms have significantly affected the extent of involvement of the folks in hate speech. Based on the findings of this study, it was concluded that social media platforms play a central role in the perpetuation of hate speeches through social media users. Hence, it was recommended that the users of social media must bear in mind the implications of involvement in the perpetuation of hate speech and its accompanying repercussions.
CoreLM: Coreference-aware Language Model Fine-Tuning

Language Models are the underpin of all modern Natural Language Processing (NLP) tasks. The introduction of the Transformers architecture has contributed significantly into making Language Modeling very effective across many NLP task, leading to significant advancements in the field. However, Transformers come with a big computational cost, which grows quadratically with respect to the input length. This presents a challenge as to understand long texts requires a lot of context. In this paper, we propose a Fine-Tuning framework, named CoreLM, that extends the architecture of current Pretrained Language Models so that they incorporate explicit entity information. By introducing entity representations, we make available information outside the contextual space of the model, which results in a better Language Model for a fraction of the computational cost. We implement our approach using GPT2 and compare the fine-tuned model to the original. Our proposed model achieves a lower Perplexity in GUMBY and LAMBDADA datasets when compared to GPT2 and a fine-tuned version of GPT2 without any changes. We also compare the models’ performance in terms of Accuracy in LAMBADA and Children’s Book Test, with and without the use of model-created coreference annotations.
PENGENALAN OBJEK SEDERHANA BERDASARKAN CIRI WARNA DENGAN METODE BACK PROPAGATION

ABSTRACTHuman being has extraordinary ability for recognizing objects based on these feature knowledge that has been observed.  Computer is a good, fast, accurate and efficient data processing device.  Computer able to solve the unstructured problem, uncomplete information and qualitative data Artificial Intelligence (AI) problem.            Pattern recognition is one of AI applications for classifying and identifying pattern or complex object by characteristics measurement or object features (color, shape, textute). A system to identify two object that have almost the same shape but defferent colors is made in this research.  Pattern recognition method is made by one of the methods of Artificial Neural Network that is supervised (Supervised Learning) that is Back Propagation. Example object in this research using apple image and orange image. Calculation result in Backpropagation shows with 14 images colour, 10x10 matriks pixel and random selected weight, so sometimes to get large error.  The resercer define if error more than 0.5 the system is not able to recognize tested images.  To get high accurately simulation data is needed. The result of this research shows that learning procces by Backpropagation, systems recognize different objects based on the color feature.
Metodología para el desarrollo del pensamiento computacional en tiempos de COVID-19

espanolEn este trabajo se describe la metodologia seguida con el proyecto ‘Piensa@ Computacion@ULLmente’ para desarrollar habilidades de pensamiento computacional en estudiantes de 4o de primaria y 2o de la Educacion Secundaria Obligatoria en tiempos de COVID-19, adaptando intervenciones en el aula y ejercicios a una modalidad sin presencialidad. La propuesta desarrollada se basa en dos vertientes. Por un lado, pildoras educativas en formato video que permiten una realizacion asincrona del proyecto, y por otro, intervenciones sincronas con los estudiantes en el aula mediante el uso de software de videoconferencia. Finalmente, se presenta como resultado una comparativa del interes generado en los participantes de este proyecto en anteriores ediciones con presencialidad y con la nueva metodologia que se describe. EnglishThis paper describes the methodology followed with the 'Piens@ Computacion@ULLmente' project to develop Computational Thinking skills considering children of KS2, 4th and KS3, 8th year in times of COVID-19, adapting classroom interventions and exercises to a telematic modality without presence. The proposal developed is based on two strands. On the one hand, educational video pills that allow an asynchronous implementation of the project, and on the other, synchronous interventions with students in the classroom using videoconferencing software. Finally, the results of a comparison studying the interest that this project has generated in the participants in previous editions face-to-face and with the new methodology that is described are presented.
Vyākarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages

While there has been significant progress towards developing NLU resources for Indic languages, syntactic evaluation has been relatively less explored.Unlike English, Indic languages have rich morphosyntax, grammatical genders, free linear word-order, and highly inflectional morphology.In this paper, we introduce Vyākarana: a benchmark of Colorless Green sentences in Indic languages for syntactic evaluation of multilingual language models.The benchmark comprises four syntax-related tasks: PoS Tagging, Syntax Tree-depth Prediction, Grammatical Case Marking, and Subject-Verb Agreement.We use the datasets from the evaluation tasks to probe five multilingual language models of varying architectures for syntax in Indic languages.Due to its prevalence, we also include a code-switching setting in our experiments.Our results show that the tokenlevel and sentence-level representations from the Indic language models (IndicBERT and MuRIL) do not capture the syntax in Indic languages as efficiently as the other highly multilingual language models.Further, our layerwise probing experiments reveal that while mBERT, DistilmBERT, and XLM-R localize the syntax in middle layers, the Indic language models do not show such syntactic localization.
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling

Multi-head attention has each of the attention heads collect salient information from different parts of an input sequence, making it a powerful mechanism for sequence modeling. Multilingual and multi-domain learning are common scenarios for sequence modeling, where the key challenge is to maximize positive transfer and mitigate negative transfer across languages and domains. In this paper, we find that non-selective attention sharing is sub-optimal for achieving good generalization across all languages and domains. We further propose attention sharing strategies to facilitate parameter sharing and specialization in multilingual and multi-domain sequence modeling. Our approach automatically learns shared and specialized attention heads for different languages and domains to mitigate their interference. Evaluated in various tasks including speech recognition, text-to-text and speech-to-text translation, the proposed attention sharing strategies consistently bring gains to sequence models built upon multi-head attention. For speech-to-text translation, our approach yields an average of $+2.0$ BLEU over $13$ language directions in multilingual setting and $+2.0$ BLEU over $3$ domains in multi-domain setting.
Peningkatan Kualitas Perbukuan dengan Penguatan Kompetensi Editor Profesional pada Start Up Penerbit di Kabupaten Banyumas

The main objectives and specific targets of this community service are: (1) mapping the level of manuscript editing competence for editors and novice writers at novice publishers (Omera Pustaka), (2) increasing the competence of editing manuscripts as indicators of professional editors, and (3) forming a forum for for the continuous development of efforts to strengthen manuscript editing competence. Today's publishing startups are starting to emerge. As a startup publisher, Omera Pustaka faces several challenges, one of which is the quality of the books published in order to be able to compete in the market. One aspect that determines the quality of a book is the role of the editor. Strengthening the competence of professional editors is very important for editors in a publisher. Professional editors can ensure that published manuscripts are print-ready and have good readability so that they have attractiveness and competitiveness in the market. The method of implementing this service uses the PALS method with the following procedures: (1) awareness, (2) capacity building, (3) mentoring, and (4) institutionalization. The activities carried out will produce quality books by strengthening the competence of professional editors at start-up publishers (Omera Pustaka). This activity produces a product in the form of a professional editor learning community which will later be included in the BSNP professional editor certification.