Reviewerly/exHarmony huggingface dataset:
Communicating in the aftermath of an earthquake: when Twitter proves to be a trustworthy and empathetic information channel.

&amp;lt;p&amp;gt;Twitter has proved to be a powerful tool for the dissemination of scientific information in the aftermath of a seismic event. During an earthquake crisis, the affected population is in need of rapid, reliable information on what has just happened and what to do next to stay safe. However, it is not rare that reliable earthquake information takes a few minutes to be accessible and shared with the population. This shortcoming can have harmful impact: every time there is a lack of information, rumors fill the void and misinformation spreads. To make matters worse, scientific communication is often jargon-laden and hence perceived as overly technical, inappropriate, and unfeeling. Effective earthquake communication must therefore be:&amp;lt;/p&amp;gt;&amp;lt;ul&amp;gt;&amp;lt;li&amp;gt;rapid and clear, to prevent fake news from spreading;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;transparent, by acknowledging uncertainty if reliable information is not available yet;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;empathetic and compassionate, to decrease anxiety and promote a sense of calming.&amp;lt;/li&amp;gt; &amp;lt;/ul&amp;gt;&amp;lt;p&amp;gt;In this light, we discuss the communication strategy of @LastQuake, the official Twitter channel (160k followers) of the Euro-Med Seismological Centre. When an earthquake strikes and is felt by the population, real-time information on the seismic event begins to be automatically published via a twitter-robot. These automatic tweets range from easily-accessible scientific information about the earthquake location and magnitude, to the shaking felt by the earthquake eyewitnesses, to the safety guidelines and &amp;amp;#8211;where applicable&amp;amp;#8211; to tsunami warnings. Our automatic tweets have little or no technical jargon. The Information is primarily accessed by users who are in the midst of responding and possibly traumatized. Hence our words, tone, and images have been carefully chosen to provide competent and appropriate communication. Meanwhile, when necessary, further tweets may be manually published to counter the onset of specific false claims and theories, or to address cultural and situational specific needs.&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our discussion will outline the current @lastquake twitter-bot environment and discuss evidence-based best practices for using Twitter for earthquake crisis communication to avoid misinformation and promote self and community efficacy.&amp;lt;/p&amp;gt;
Dataset Dynamics via Gradient Flows in Probability Space

Various machine learning tasks, from generative modeling to domain adaptation, revolve around the concept of dataset transformation and manipulation. While various methods exist for transforming unlabeled datasets, principled methods to do so for labeled (e.g., classification) datasets are missing. In this work, we propose a novel framework for dataset transformation, which we cast as optimization over data-generating joint probability distributions. We approach this class of problems through Wasserstein gradient flows in probability space, and derive practical and efficient particle-based methods for a flexible but well-behaved class of objective functions. Through various experiments, we show that this framework can be used to impose constraints on classification datasets, adapt them for transfer learning, or to re-purpose fixed or black-box models to classify -- with high accuracy -- previously unseen datasets.
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model

Knowledge distillation (KD) is a successful approach for deep neural network acceleration, with which a compact network (student) is trained by mimicking the softmax output of a pre-trained high-capacity network (teacher). In tradition, KD usually relies on access to the training samples and the parameters of the white-box teacher to acquire the transferred knowledge. However, these prerequisites are not always realistic due to storage costs or privacy issues in real-world applications. Here we propose the concept of decision-based black-box (DB3) knowledge distillation, with which the student is trained by distilling the knowledge from a black-box teacher (parameters are not accessible) that only returns classes rather than softmax outputs. We start with the scenario when the training set is accessible. We represent a sample's robustness against other classes by computing its distances to the teacher's decision boundaries and use it to construct the soft label for each training sample. After that, the student can be trained via standard KD. We then extend this approach to a more challenging scenario in which even accessing the training data is not feasible. We propose to generate pseudo samples distinguished by the teacher's decision boundaries to the largest extent and construct soft labels for them, which are used as the transfer set. We evaluate our approaches on various benchmark networks and datasets and experiment results demonstrate their effectiveness. Codes are available at: this https URL.
Nostalgia of the Beginnings

Henry David’s Thoreau book Walden recreates in a literary form the period that the author spent in the woods near Walden Pond. Being a transcendentalist influenced by Emerson’s philosophy, Thoreau reiterates the essential role that nature has in the spiritual and moral development of man. In Walden the author describes a return to a primordial age in which man lived in a state of wonder before the beauty of the universe and in a permanent communion with it. The sacredness of nature is rendered through ritual gestures that accompany man on his road to revelation. Thus, the period of time spent by Thoreau near Walden Pond acquires the qualities of an initiation during which man rediscovers his self and undergoes a spiritual awakening.
New literacy and data-future in education: advanced technology smart big-data

The content of the study is presented: how the countries of the world are developing digital educational platforms, digital educational data services, and technologies for managing the development of education based on smart data. It reveals: how national education systems are revising educational approaches in order to create opportunities to develop new literacy and competencies of the future. It is presented: how the development of big-data, smart-data, block-chain technologies will lead to a change in assessment tools, regulations for converting educational results at different levels of education and in different forms of education (formal and non-formal education). It is described: how the countries of the world implement models of educational data analysis in education based on the development of digital platforms and services for the analysis of educational data and educational statistics.
Assessment of the effectiveness of correspondence teacher education

The article examines one of the features of the development of modern society is the process of informatization, which has now acquired a global character. It has been proven that under the influence of informatization, changes are currently taking place in all spheres of life. One of the elements of informatization are new information technologies, the use of which allows finding new approaches to solving various problems. The consequence of the general informatization of society is the informatization of education, one of the forms of which is distance learning. Distance education technology is an element of distance education. However, along with the intensive introduction of new educational technologies into the educational process, the question arises: how effective is the distance learning system?
USING STORYTELLING TO ENHANCE LOWER SECONDARY STUDENTS’ LISTENING COMPREHENSION ABILITY

This study investigates the impacts of Storytelling technique on helping students improve their listening comprehension ability, and students’ attitudes toward the use of the technique in listening lessons. The study followed the quasi-experimental research model, with 60 students participated in the control and experimental groups. Data were collected through two instruments including pretest and posttest, and pre and post questionnaires distributed before and after the research period. Findings from the data analysis showed that Storytelling was effective in helping students improve their listening comprehension. Storytelling helped students understand the details of the stories more easily, facilitated students in learning vocabulary and pronunciation, promoted their cognitive process in figuring out the meaning. Students also acknowledged that storytelling made learning listening more interesting to them.
Student Suite+, A Closed Domain Question Answering System for Educational Domain

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI). It is a study of how machines understand the language of humans. It aims to build systems that can understand text and perform tasks like language translation and classifying a topic. NLP has many applications. NLP tools help to process unstructured data. QuestionAnswering (QA) system or information retrieval system falls under the category of NLP. The QA system mainly involves two methods, question analysis and answer extraction. Intelligent QA systems have always outperformed the non-intelligent QA systems. It is a neural network that is incorporated with the QA system to make it intelligent. Neural networks(NN) are a series of algorithms that are used to mimic the operations of a human brain to understand the relationships between data. NN can learn by themselves and produce the output that is not limited to the input provided to them.
A Deep CFHT Optical Search for a Counterpart to the Possible Neutron Star Black Hole Merger GW190814

We present a wide-field optical imaging search for electromagnetic counterparts to the likely neutron star–black hole (NS–BH) merger GW190814/S190814bv. This compact binary merger was detected through gravitational waves by the LIGO/Virgo interferometers, with masses suggestive of an NS–BH merger. We imaged the LIGO/Virgo localization region using the MegaCam instrument on the Canada–France–Hawaii Telescope (CFHT). We describe our hybrid observing strategy of both tiling and galaxy-targeted observations, as well as our image differencing and transient detection pipeline. Our observing campaign produced some of the deepest multiband images of the region between 1.7 and 8.7 days post-merger, reaching a 5σ depth of g > 22.8 (AB mag) at 1.7 days and i > 23.1 and i > 23.9 at 3.7 and 8.7 days, respectively. These observations cover a mean total integrated probability of 67.0% of the localization region. We find no compelling candidate transient counterparts to this merger in our images, which suggests that the lighter object was tidally disrupted inside of the BH’s innermost stable circular orbit, the transient lies outside of the observed sky footprint, or the lighter object is a low-mass BH. We use 5σ source detection upper limits from our images in the NS–BH interpretation of this merger to constrain the mass of the kilonova ejecta to be M ej ≲ 0. 015M ⊙ for a “blue” () kilonova and M ej ≲ 0. 04M ⊙ for a “red” () kilonova. Our observations emphasize the key role of large-aperture telescopes and wide-field imagers such as CFHT MegaCam in enabling deep searches for electromagnetic counterparts to gravitational-wave events.
A Psychophysical Learning System

Chapter 9 closes the loop with Chapter 2 to demonstrate the methods and techniques that can achieve the goals and objectives presented in Chapter 2. The attributes of the total system are summarized and supported with research that has examined its effectiveness in real-world applications. The attributes of the learning engagement, experience, and environment are discussed and supported with available practical and action research. As in the construction of cognition, this chapter is constructed in a matrixed model where the interactions across categories are explicit from multiple perspectives, which reveals the interactions across the categories of content.
The Use of Adaptive Spatial Filters in the Analysis of Medical Images

Although there are many methods in the literature to eliminate noise from images, finding new methods remains a challenge in the field and, despite the complexity of existing methods, many of the methods do not reach a sufficient level of applicability, most often due to the relatively high calculation time. In addition, most existing methods perform well when the processed image is adapted to the algorithm, but otherwise fail or results in significant artifacts. The context of eliminating noise from images is similar to that of improving images and for this reason some notions necessary to understand the proposed method will be repeated. An adaptive spatial filter in the wavelet domain is proposed by soft truncation of the wavelet coefficients with threshold value adapted to the local statistics of the image and correction based on the hierarchical correlation map. The filter exploits, in a new way, both the inter?band and the bandwidth dependence of the wavelet coefficients, considering the minimization of computational resources. The proposed method uses soft truncation of coefficients, where the threshold value is calculated for each coefficient separately based on local statistics in the wavelet domain.
A Book Reviews of Foucault’s Discipline and Punish with Educational Refletions

Foucault is the original author of the book (Surveiller et punir: Naissance de la prison), and the original edition is written in French [1].And then the Eng-
On Software Security Requirements Elicitation and Analysis Methods

Security requirements play an important role to protect valuable data and information from unauthorized users. The elicitation of the security requirements in the early phase of the software development process can help the software engineers to successfully develop the secure information system. Different methods have been developed for the elicitation of the security requirements like multilateral security requirements analysis, software quality requirements engineering, knowledge acquisition for automated specifications, etc. The objective of this paper is to evaluate the different software Security Requirements Elicitation and Analysis (SecREA) methods based on the following parameters: (a) stakeholders’ participation, (b) identification of the functional requirements which needs more security, (c) selection and prioritization of the security requirements under crisp and fuzzy environment, (d) common activities in SecREA methods. After evaluating the SecREA methods, we discuss the scope for future work.
INTEGRATION PROCESSES IN PEDAGOGY AND EDUCATION: ESSENCE, PROBLEMS, SOLUTION

The article discusses the integration capabilities that are carried out using synthesis as a result of obtaining a new system characterized by integrity and higher functionality. It is substantiated that the versatility, diversity of manifestations of integration has led to the appearance of its numerous interpretations, which mutually complement and reveal the essence of this concept. The occurrence of the phenomenon of integration is a natural characteristic of the modern level of development of pedagogical science and education. The article provides a logical and meaningful analysis of the definitions of the concept of "integration", as well as an analysis of the trends in the forms of integration in the education system, analyzes approaches to determining the essence of the concept of "integration" in the educational process, identifies integrative and pedagogical concepts. The connection of pedagogical science and pedagogical practice through the connection of the main types of pedagogical knowledge is presented. The authors conclude that the integration processes without fail lead to the formation of a new whole and at the same time to the enrichment of the components of integrable components.
Covid-19 Thesaurus

This bilingual thesaurus (French-English), developed at Inist-CNRS, covers the concepts from the emerging COVID-19 outbreak which reminds the past SARS coronavirus outbreak and Middle East coronavirus outbreak. This thesaurus is based on the vocabulary used in scientific publications for SARS-CoV-2 and other coronaviruses, like SARS-CoV and MERS-CoV. It provides a support to explore the coronavirus infectious diseases. The thesaurus can be browsed and queried by humans and machines on the Loterre portal (https://www.loterre.fr), via an API and an rdf triplestore. It is also downloadable in PDF, SKOS, csv and json-ld formats. The thesaurus is made available under a CC-by 4.0 license.
Machine Learning for Scientific Discovery

Machine Learning algorithms are good tools for both classification andprediction purposes. These algorithms can further be used for scientificdiscoveries from the enormous data being collected in our era. We present waysof discovering and understanding astronomical phenomena by applying machinelearning algorithms to data collected with radio telescopes. We discuss the useof supervised machine learning algorithms to predict the free parameters ofstar formation histories and also better understand the relations between thedifferent input and output parameters. We made use of Deep Learning to capturethe non-linearity in the parameters. Our models are able to predict with lowerror rates and give the advantage of predicting in real time once the modelhas been trained. The other class of machine learning algorithms viz.unsupervised learning can prove to be very useful in finding patterns in thedata. We explore how we use such unsupervised techniques on solar radio data toidentify patterns and variations, and also link such findings to theories,which help to better understand the nature of the system being studied. Wehighlight the challenges faced in terms of data size, availability, features,processing ability and importantly, the interpretability of results. As ourability to capture and store data increases, increased use of machine learningto understand the underlying physics in the information captured seemsinevitable.
Cognitive Analysis on Web Server Log Metaheuristic Algorithm and Distributed ARM

Web use is increasing daily, Web use mining (WUM) and frequent pattern mining make it easier to assess cognitive evaluation from Web server log. This cognitive analysis is helping the organization decision makers to take on strategy decisions. Association rule mining (ARM) is considered one the most excellent method for identifying frequent patterns from data source. Here our aim is to find frequently used elements such as urls, the greatest number of used urls, set of urls. These elements are browsed together in the way as we can identify website consumer behavior. In this paper we have proposed an algorithm that are a comprehensive approach of Metaheuristic (Genetic algorithm) and Multicore processing named as GMARM (Genetic Multi-core Association Rule Mining). Here we enter web usage data to a preprocessing tool which is Genetic algorithm-based preprocessing, then we apply association rule mining to find out user behavior pattern using MARM algorithm. MARM algorithm finds gain of multi-core processor, preprocessed data passed to distributed processors.
Factors Affecting Community Behavior Using Energy Efficient and Environmentally Friendly

Abstract. The objectives of this study are (1) to find knowledge about energy saving and environmentally friendly electrical energy, attitudes towards electric energy, and motivation to use energy efficient and environmentally sound for the people living in type 70 houses in Makassar City; (2) to find out the behavior of the community using electricity that is economical and environmentally friendly in the people living in type 70 houses in Makassar City; (3) to find the influence of knowledge about electrical energy that is environmentally friendly, attitudes towards electrical energy, and motivation to use energy that is economical and environmentally sound individually and collectively on people's behavior using economical and environmentally friendly electrical energy. inhabit the house type 70 in Makassar City. This research is classified as a correlational research located in a housing complex type 70 in Makassar City. The sample size was 50 heads of household selected by purposive random sampling method. The results of the study are (1) Knowledge, attitudes, and motivation of the people living in type 70 housing in Makassar City using efficient and environmentally friendly electrical energy are in the medium category, (2) The behavior of the people who inhabit type 70 housing in Makassar City uses Electric energy that is economical and environmentally sound is in the medium category, (3) Knowledge, attitudes, and motivation of the people living in type 70 housing in Makassar City using electricity that is economical and environmentally sound have a significant effect both individually and collectively -same on the behavior of using electric energy that is efficient and environmentally friendly. Keywords: Knowledge, attitudes, motivation and behavior of energy saving and  environmentally friendly electrical energy
METHODOLOGICAL FEATURES OF TEACHING STUDENTS THE BASICS OF ROBOTICS

The main tasks of modern education are: training, education and development. In their professional activities, the teacher guarantees the continuous implementation of these processes, their harmonious combination and strives to ensure the effectiveness of the educational process. The approach to competence in General secondary education objectively corresponds to social expectations in the field of education and the interests of participants in the educational process. The competence approach is an approach that takes into account learning outcomes, so the result of learning is not a set of information received, but the ability of students to respond to various problem situations. Therefore, in the secondary education system, a set of tasks on the basics of robotics is used as a tool for teachers to achieve educational goals
Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction

In this paper, we propose a new dimension reduction (DR) algorithm called ensemble graph-based locality preserving projections (EGLPP); to overcome the neighborhood size k sensitivity in locally preserving projections (LPP). EGLPP constructs a homogeneous ensemble of adjacency graphs by varying neighborhood size k and finally uses the integrated embedded graph to optimize the low-dimensional projections. Furthermore, to appropriately handle the intrinsic geometrical structure of the multi-view data and overcome the dimensionality curse, we propose a generalized multi-manifold graph ensemble embedding framework (MLGEE). MLGEE aims to utilize multi-manifold graphs for the adjacency estimation with automatically weight each manifold to derive the integrated heterogeneous graph. Experimental results on various computer vision databases verify the effectiveness of proposed EGLPP and MLGEE over existing comparative DR methods.
Errors in Students’ Writing Composition in Simple Present Tense ‘My Daily Activity’

This research aims to find out the types and the most dominant types of error in terms of grammar, punctuation and spelling found in Students’ Writing Composition in Simple Present Tense. This study applies mixed method approach to research. This study uses documentation as a method of collecting data. The data demonstrate that the students’ errors in writing composition of Simple Present Tense consist of three aspects of writing: grammar, punctuation and spelling. In terms of grammar, there are 66 data or 56.89 %, which consist of some errors in word class (part of speech) such as in verb (25 data or 37.88 %), determiner/article (16 data or 24.24 %), preposition (15 data or 13.64 %), noun (9 data or 13.64 %) and pronoun (1 data or 1.51 %). In terms of punctuation, there are 28 data or 28.14 %, while in spelling there are 22 data or 18.97 %.