Reviewerly/exHarmony huggingface dataset:
СУТНІСНІ ЕЛЕМЕНТИ Й ХАРАКТЕРИСТИКИ УПРАВЛІНСЬКОЇ КУЛЬТУРИ КЕРІВНИКА ОСВІТНЬОЇ ОРГАНІЗАЦІЇ

Summary. Introduction. The topicality of the problem raised in the title of the article is due to several reasons. Despite three decades of active study of management culture, researchers do not have a common position on the closest generic trait and species differences in the definitions of “a manager’s managerial culture”, defining a set of key elements and essential features of this phenomenon. Given the theoretical significance of this problem, which is primarily the need to outline the system-forming characteristics of the concept of “managerial culture of heads of educational organizations”, and its application, in particular in educational and pedagogical practices of training future managers of education, in the system of training. The aim of the article was to find the optimal research focus to clarify the essential elements and characteristics of managerial culture, i.e. those theoretical-and-methodological constructs that adequately reflect the most complex reality of management of educational organizations and thus contribute to a more accurate and deeper understanding of this concept.Methods. To achieve this goal, a comprehensive toolkit of general scientific methods was involved, in particular, analytical-and-synthetic techniques and methods of comparative study of the object of scientific knowledge. In their work, the authors relied on a critical assessment and verification of the results obtained by other scientists, as well as their own observations in the field of management culture of heads of establoishments of education and personal management experience.Results. The definition of the concept of “managerial culture of heads of educational organizations” is clarified by outlining two fundamental loci of its existence – professional-and-personal culture and professional-and-managerial activities. To adequately understand this concept, its basic elements such as power, responsibility, authority have been identified and analyzed, and five groups of essential features of educational managers’ managerial culture have been revealed: general and professional competencies; values and ambitions; subjective (individual) managerial experience; reflection; image.As a result of the study, a general conclusion was made: the integration of all the above mentioned essential elements and key features of managerial culture fills it with special meaning, which indicates the leader’s competence and professionalism, and most importantly – his/her effectiveness as a manager, provides the managerial activity with the unity of its external and internal characteristics, enables creative self-realization of the individual in management.
Semantic Mapping in Language Teaching in term of Memorizing Vocabularies

When students learn English, they usually faced with many difficulties not only in specific language skills but also in vocabulary memorizing. Because of their learning habits and learning strategies, they failed to memorize words for a long time and to recall words when necessary. Based on literature review, it is found that semantic mapping has had good effects on vocabulary learning; especially it improves the retention and retrieval of word meanings.. The results indicated that the students in the experimental group outperformed those in the control condition in retaining word meanings. The results also proved that the students had positive attitudes towards semantic mapping. This leads to the implication that the semantic mapping can improve high school students’ vocabulary retention and is promising to vocabulary teaching and learning.
Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering.

Human knowledge is collectively encoded in the roughly 6500 languages spoken around the world, but it is not distributed equally across languages. Hence, for information-seeking question answering (QA) systems to adequately serve speakers of all languages, they need to operate cross-lingually. In this work we investigate the capabilities of multilingually pretrained language models on cross-lingual QA. We find that explicitly aligning the representations across languages with a post-hoc finetuning step generally leads to improved performance. We additionally investigate the effect of data size as well as the language choice in this fine-tuning step, also releasing a dataset for evaluating cross-lingual QA systems.
SciCap: Generating Captions for Scientific Figures.

Researchers use figures to communicate rich, complex information in scientific papers. The captions of these figures are critical to conveying effective messages. However, low-quality figure captions commonly occur in scientific articles and may decrease understanding. In this paper, we propose an end-to-end neural framework to automatically generate informative, high-quality captions for scientific figures. To this end, we introduce SCICAP, a large-scale figure-caption dataset based on computer science arXiv papers published between 2010 and 2020. After pre-processing – including figure-type classification, sub-figure identification, text normalization, and caption text selection – SCICAP contained more than two million figures extracted from over 290,000 papers. We then established baseline models that caption graph plots, the dominant (19.2%) figure type. The experimental results showed both opportunities and steep challenges of generating captions for scientific figures.
Detecting Community Sensitive Norm Violations in Online Conversations

Online platforms and communities establish their own norms that govern what behavior is acceptable within the community. Substantial effort in NLP has focused on identifying unacceptable behaviors and, recently, on forecasting them before they occur. However, these efforts have largely focused on toxicity as the sole form of community norm violation. Such focus has overlooked the much larger set of rules that moderators enforce. Here, we introduce a new dataset focusing on a more complete spectrum of community norms and their violations in the local conversational and global community contexts. We introduce a series of models that use this data to develop context- and community-sensitive norm violation detection, showing that these changes give high performance.
Classical Planning with Avoid Conditions

It is often natural in planning to specify conditions that should be avoided, characterizing dangerous or highly undesirable behavior. PDDL3 supports this with temporal-logic state trajectory constraints. Here we focus on the simpler case where the constraint is a non-temporal formula ? - the avoid condition - that must be false throughout the plan. We design techniques tackling such avoid conditions effectively. We show how to learn from search experience which states necessarily lead into ?, and we show how to tailor abstractions to recognize that avoiding ? will not be possible starting from a given state. We run a large-scale experiment, comparing our techniques against compilation methods and against simple state pruning using ?. The results show that our techniques are often superior.
Virtuality as a New Ontological Model of the World

In today’s rapidly changing world, the debate over the scientific landscape of the world is heating up, and in this context, virtuality, which represents the world in an orderly, systematic, hierarchical way, is seen as a new model.
Aprendizaje cooperativo: condiciones para la intervención socioemocional en alumnos de preescolar

espanolEste articulo presenta la valoracion de las habilidades socioemocionales de alumnos de nivel preescolar en areas de atencion para su intervencion en el ambito educativo a traves del aprendizaje cooperativo, como parte de la formacion profesionalizante de la Maestria en Gestion del Aprendizaje de la Universidad Veracruzana, region Poza Rica-Tuxpan. Los participantes la conforman 18 alumnos de preescolar de entre 4 y 5 anos de un jardin de ninos de la ciudad de Poza Ria, Veracruz, Mexico. El analisis realizado con el uso de herramientas como entrevistas con maestras, encuestas con los padres de familia y cuestionario que explora las habilidades socioemocionales desde las conductas observables de los alumnos, con 43 items categorizados en cuatro dimensiones: habilidad para relacionarse, comunicacion, expresion de emociones y autoafirmacion; revelaron que los alumnos que presentan conflicto en dichas areas, afectan negativamente al grupo y al bienestar escolar, aunado a la inminente crisis por la pandemia por Covid-19 que implica un reto para fomentar el desarrollo socioemocional por medio de herramientas digitales lo que a su vez infiere una fuerte critica social y academica de las practicas tradicionales de ensenanza-aprendizaje. EnglishThis article presents the assessment of the socio-emotional skills of preschool students in areas of attention for their intervention in the educational field through cooperative learning, as part of the professional training of the Master in Learning Management of the Veracruzana University, region Poza Rica-Tuxpan. The participants are made up of 18 preschool students between 4 and 5 years old from a kindergarten in the city of Poza Ria, Veracruz, Mexico. The analysis carried out with the use of tools such as interviews with teachers, surveys with parents and a questionnaire that explores socio-emotional skills from the observable behaviors of students, with 43 items categorized in four dimensions: ability to relate, communication, expression of emotions and self-affirmation; revealed that students who present conflict in these areas negatively affect the group and school well-being, coupled with the imminent crisis due to the Covid- 19 pandemic, which implies a challenge to promote socio-emotional development through digital tools, which in turn It instead infers a strong social and academic critique of traditional teaching-learning practices.
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning

Privacy is of primary importance when it comes to the Financial Domain as the data is highly confidential and no third party can be having access to it. Natural Language Processing (NLP) techniques can be applied for text classification and entity detection purposes in financial domains like customer feedback sentiment analysis, invoice entity detection, categorisation of financial documents by type etc. Due to the sensitive nature of such data, privacy measures need to be taken for handling and training large models with such data. In this work, we propose a contextualized transformer (BERT and RoBERTa) based text classification model integrated with privacy features like Differential Privacy (DP) and Federated Learning (FL). We present how to privately train NLP models and desirable privacy utility trade-offs and evaluate it on the Financial Phrase Bank dataset.
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications

Most real world applications require dealing with stochasticity like sensor noise or predictive uncertainty, where formal specifications of desired behavior are inherently probabilistic. Despite the promise of formal verification in ensuring the reliability of neural networks, progress in the direction of probabilistic specifications has been limited. In this direction, we first introduce a general formulation of probabilistic specifications for neural networks, which captures both probabilistic networks (e.g., Bayesian neural networks, MC-Dropout networks) and uncertain inputs (distributions over inputs arising from sensor noise or other perturbations). We then propose a general technique to verify such specifications by generalizing the notion of Lagrangian duality, replacing standard Lagrangian with multipliers that can be arbitrary functions of the activations at a given layer. We show that an optimal choice of functional leads to exact verification (i.e., sound and complete verification), and for specific forms of multipliers, we develop tractable practical verification algorithms. We empirically validate our algorithms by applying them to Bayesian Neural Networks (BNNs) and MC Dropout Networks, and certifying properties such as adversarial robustness and robust detection of out-of-distribution (OOD) data. On these tasks we are able to provide significantly stronger guarantees when compared to prior work -- for instance, for a VGG-64 MC-Dropout CNN trained on CIFAR-10, we improve the certified AUC (a verified lower bound on the true AUC) for robust OOD detection (on CIFAR-100) from $0\% \rightarrow 29\%$. Similarly, for a BNN trained on MNIST, we improve on the robust accuracy from $60.2\% \rightarrow 74.6\%$. Further, on a novel specification -- distributionally robust OOD detection -- we improve the certified AUC from $5\% \rightarrow 23\%$.
PENGGUNAAN PBL DENGAN TUTOR SEBAYA DAN Hp DALAM UPAYA MENINGKATKAN PEMBELAJARAN POLINOMIAL

At first the teacher saw the participants' low mathematics learning outcomes. Of the several problems, students' understanding problems in mathematics learning, teacher learning are still conventional, and most students are less active in participating in mathematics learning.The teacher uses the Problem Based Learning (PBL) model with peer tutors utilizing Information and Communication Technology facilities, namely Mobile (Hp). PBL emphasizes more on motivating students, in problem solving activities that can explain and solve problems given by the teacher, in addition to learning from the teacher, students get learning from their friends, so they become active. From seeing the activeness of students in the initial condition, they were less active to being active in cycle 1 and cycle 2. Learning activeness increased to be active by 51% in cycle 1, and to 63% in cycle 2. The increase in student learning achievement can be seen from the average Classical mean of learning outcomes from the initial conditions 46.1 to the final conditions 70.5. The completeness of learning the initial conditions of the 33 students was low, namely 6%, increased in cycle 1 was obtained 33.3% and increased to 63.6% in cycle 2. The results achieved from the Polynomial mathematics learning activities of class XI IPA SMA Negeri 3 Salatiga by using PBL with Peer Tutors and Hp shows that it can increase the activeness, process skills, and learning achievement of students.
Assessment of Phishing Email or URL Utilizing Machine Learning

Phishing has become substantially bigger risk problem in online networking; and is usually performed by means of email mocking. In this paper we have focused mainly on the awareness on latest approach of real time phishing electronic mail type using system gaining knowledge of set of rules. We use machine leaning algorithm on spam base dataset random woodland set of rules work best which give true positive 97.2% and false negative is 0.88% and give efficiently class 94.82% and incorrectly type 5.17%.
Controllable Semantic Parsing via Retrieval Augmentation

In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its predictions on certain targeted queries. While we can introduce new training examples exhibiting the target behavior, a mechanism for enacting such behavior changes without expensive model re-training would be preferable. To this end, we propose ControllAble Semantic Parser via Exemplar Retrieval (CASPER). Given an input query, the parser retrieves related exemplars from a retrieval index, augments them to the query, and then applies a generative seq2seq model to produce an output parse. The exemplars act as a control mechanism over the generic generative model: by manipulating the retrieval index or how the augmented query is constructed, we can manipulate the behavior of the parser. On the MTOP dataset, in addition to achieving state-of-the-art on the standard setup, we show that CASPER can parse queries in a new domain, adapt the prediction toward the specified patterns, or adapt to new semantic schemas without having to further re-train the model.
Press Freedom Monitor: Detection of Reported Press and Media Freedom Violations in Twitter and News Articles

Freedom of the press and media is of vital importance for democratically organised states and open societies.We introduce the Press Freedom Monitor, a tool that aims to detect reported press and media freedom violations in news articles and tweets.It is used by press and media freedom organisations to support their daily monitoring and to trigger rapid response actions.The Press Freedom Monitor enables the monitoring experts to get a swift overview of recently reported incidents and it has performed impressively in this regard.This paper presents our work on the tool, starting with the training phase, which comprises defining the topic-related keywords to be used for querying APIs for news and Twitter content and evaluating different machine learning models based on a training dataset specifically created for our use case.Then, we describe the components of the production pipeline, including data gathering, duplicates removal, country mapping, case mapping and the user interface.We also conducted a usability study to evaluate the effectiveness of the user interface, and describe improvement plans for future work.
Open computational social science

We discuss how computational social scientists can achieve high standards of accessibility, transparency, replicability, and reproducibility by adhering to four open science principles: open practices, open data, open tools, and open access. For each principle, we discuss the underlying motivation, challenges, and potential solutions for computational social scientists. Open practices refer to honest and transparent specification of all steps in the data processing and data analysis. We recommend that computational social scientists share their materials and analysis scripts. Open data refers to data that are publicly available, and we urge computational social scientists to share their data whenever possible. When data cannot be shared, researchers ought to be explicit about the conditions of its availability. Open tools refer to software that is both free and has its source code available to others. We recommend that computational social scientists share their code, software, and educational materials whenever possible so that other people can learn computational techniques. Finally, open access is making the products of one’s research process publicly available. We encourage computational social scientists to use open-access outlets for the ultimate publication of work when feasible and to self-archive their research products regardless.
Uncovering the Limits of Text-based Emotion Detection.

Identifying emotions from text is crucial for a variety of real world tasks. We consider the two largest now-available corpora for emotion classification: GoEmotions, with 58k messages labelled by readers, and Vent, with 33M writer-labelled messages. We design a benchmark and evaluate several feature spaces and learning algorithms, including two simple yet novel models on top of BERT that outperform previous strong baselines on GoEmotions. Through an experiment with human participants, we also analyze the differences between how writers express emotions and how readers perceive them. Our results suggest that emotions expressed by writers are harder to identify than emotions that readers perceive. We share a public web interface for researchers to explore our models.
Test and Analysis of Electromagnetic Sensitivity Mechanism Characteristics of Motor Control System

With the wide application of motor control systems, the performance of the motor has become more stable. However, in the face of increasingly complex electromagnetic environment, the electromagnetic compatibility problems of motor control systems become more and more severe. This article takes the motor control system composed of the stepping motor as the research object. With experimental demonstration, the electromagnetic sensitivity mechanism of the stepper motor control system under typical interference signals is studied, and the electromagnetic sensitivity threshold law and sensitivity model of the stepper motor control system are proposed. This sensitive model is related to the length of the interference time and the power of the interference signal.
MEMBANGUN SELF-AWARENESS MELALUI EDUKASI VIRTUAL BAGI SISWA SISWI SEKOLAH DASAR DI KOTA CIREBON

Self-awareness is predominant for every individual, including current situation in particular, where Covid-19 outbreak has globally hit people. The attitude is also expected to be experienced by every student in all levels. The facts that there are still many students who do not realize the importance of having self-awareness in maintaining and implementing health protocols during learning from home or online learning. Accordingly, Community Service Program (PKM) team from Politeknik Harapan Bersama conducted virtual education. The program was carried out with the aim of introducing and building self-awareness among students at MI Darul Hikam Cirebon in implementing health protocols in everyday life. Storytelling was chosen as the method to develop children's affective (feeling), social, and cognitive (appreciation) aspects to raise the awareness. A total of 100 of 6 th grade students and 3 homeroom teachers from the school participated in the agenda. The storytelling began with brainstorming the title and several pictures related to the situation before and during the Covid-19 pandemic. Core activities were dedicated to classically perform the 5M movement (Wear Masks, Wash hands with soap and water, Physical Distancing, Reduce Mobility and Avoid crowd) in bilingual. The enthusiasm was shown by the students in demonstrating the movements several times. They were able to mention sequent 5M movement in good English pronunciation. In addition, the students virtually practiced of washing hands and wearing masks well. This shows that the implementation of Community Service Program (PKM) ran smoothly in accordance with the above aim.
RETRACTED: Bayesian approach to incremental batch learning on forest cover sensor data for multiclass classification

Deep neural networks can be used to perform nonlinear operations at multiple levels, such as a neural network that is composed of many hidden layers. Although deep learning approaches show good results, they have a drawback called catastrophic forgetting, which is a reduction in performance when a new class is added. Incremental learning is a learning method where existing knowledge should be retained even when new data is acquired. It involves learning with multiple batches of training data and the newer learning sessions do not require the data used in the previous iterations. The Bayesian approach to incremental learning uses the concept of the probability distribution of weights. The key idea of Bayes theorem is to find an updated distribution of weights and biases. In the Bayesian framework, the beliefs can be updated iteratively as the new data comes in. Bayesian framework allows to update the beliefs iteratively in real-time as data comes in. The Bayesian model for incremental learning showed an accuracy of 82%. The execution time for the Bayesian model was lesser on GPU (670 s) when compared to CPU (1165 s).