Reviewerly/exHarmony huggingface dataset:
Teamwork As A Highly Effective Approach To Teaching Majors In Higher Education

This work is dedicated to the pedagogical problem.This article presents an algorithm for arranging students' teamwork in such majors as "Mechanics" and "General Physics Workshop".Freshmen who are engaged in teamwork adapt more easily to a new social and educational environment.Also, teamwork increases the effectiveness of learning.The basic forms of working with students of technical specialties are shown.The problems of student learning and possible ways to solve them are considered.The course and the material and technical base allow every student to have his or her individual manual for the laboratory work.every student has to work with every groupmate irrespective of whether there is fondness or not.Psychologically, this helps develop tolerance and the ability to work with any partner.Students with the initially low or average level of knowledge need to be engaged in teamwork.Learning difficulties can hinder adaptation.Consequently, students experience confusion, self-doubt, and lack of self-esteem.
Lin: Unsupervised Extraction of Tasks from Textual Communication

Commitments and requests are a hallmark of collaborative communication, especially in organizational settings.Identifying specific tasks being committed to or requests from emails and chat messages can enable important downstream tasks, such as producing to-do lists, reminders, and calendar entries.State-of-the-art approaches for task identification rely on large annotated datasets, which are not always available, especially for domain-specific tasks.Accordingly, we propose Liṅ, an unsupervised approach of identifying tasks that leverages dependency parsing and VerbNet.Our evaluations show that Liṅ yields comparable or more accurate results than supervised models on domains with large training sets, and maintains its excellent performance on unseen domains.
MEDIA EDUCATION: GLOBAL PROSPECTS AND DEVELOPMENTS IN UKRAINE (ON THE EXPAMPLE OF THE IREX “LEARN AND DISTINGUISH” PROJECT)

The paper highlights the strategic guidelines for the media education development outlined by international organizations (UNESCO, EU). The authors analyse the implementation of media education in Ukraine. The authors analyse the implementation of the media education in the Ukrainian education starting from the adoption of the Concept on the Implementation of Media Education in Ukraine (2010). The implemen-tation of media education into the secondary education is confirmed by example of the State Standard of Primary Education (2018) and Typical Educational Programmes for 1st-2nd and 3rd-4th grades developed under guidance of O. Savchenko (2019). The authors analyse the findings of the project «Learn and Distinguish» (L2D), implemented by the International Research & Exchanges Board (IREX) in partnership with the Academy of Ukrainian Press and StopFake aimed at integrating media and information literacy skills into curricula. It was piloted in 50 schools in Ukraine, reaching approximately 5,425 students in 8th and 9th grades. The authors made conclusions that value of the L2D lies in transferring the participants' media knowledge. No less important is the project's input into capacity development. The authors underline the added value of the project. It promotes further harmonization of the Ukrainian education with the trends/standards of the world community providing the development of the efficient media education system in Ukraine. And secondly, it serves for the dissemination of innovative knowledge and practices at the horizontal level (between teachers, schools, students).
Organization of E-Learning with Incorporation of an Online Component

The purpose of this article is to formulate recommendations for organizing network form of learning using the online component. The advantages of using the online component in the educational process are outlined. Methodology: in the process of preparing the article, the dialectical method was used, which consists in the application of methods of analysis, scientific generalization. Conclusions. The authors consider the organization of network form of learning as a promising area of joint activities of educational organizations and other organizations that have the resources necessary for the implementation of educational programs. The article formulates the key characteristics of network form of learning, analyzes changes in the regulation of issues of organizing network learning. Scientific and practical significance. Taking these aspects into account, the authors propose possible models for including the online component in the organization of network form of learning. Issues are considered in relation to higher education.
Multi Image Steganography using Distributed LSB Algorithm and Secret Text Recovery on Stego Image Corruption

In this proposed research work, an attempt has been made to use multiple image files for steganography encoding along with the capability of secret text recovery in the event of any image corruption during the transit. This algorithm is effective on the security factor of secret image since the embedded checksum will validate for any unauthorized users or intruders attempt to corrupt the picture in any aspect. If any of the stego image underwent any steganalysis or MiM attack, then this proposed algorithm can effectively regenerate the content of one stego image using other intact stego images received in the receiving end.
Emotional Intelligence As A Factor Of Students’ Family Self-Determination Development

Emotional intelligence as a factor influencing young people's family self-determination has not been sufficiently studied, and the question of the possibility of its development among students in the sphere of family relations also remains open.The purpose of our research is to reveal peculiarities of connection between the level of emotional intelligence development and structural and content characteristics of family self-determination at youthful age.The research was carried out at the basis of Astrakhan State University.The study was attended by 300 students aged from 17 to 19 years old.There were 84 young men and 216 girls among them.With the help of N. Hall's questionnaire «Diagnostics of emotional intelligence» it was established that 65% of respondents have a low level, 31.7% -average level, 3.3% -high level of emotional intelligence development.Emotional intelligence determines the development of value and emotional, regulatory and behavioral, motivational, and reflexive components of students' family self-determination.Emotional intelligence is an important factor determining the development of family self-determination of student youth.Preparing for family and marriage of modern youth will be effective in organizing psychological and pedagogical support aimed at the development of emotional intelligence in the sphere of family relations.
Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot

In this paper, we introduce Annobot: a platform for annotating and creating datasets through conversation with a chatbot.This natural form of interaction has allowed us to create a more accessible and flexible interface, especially for mobile devices.Our solution has a wide range of applications such as data labelling for binary, multi-class/label classification tasks, preparing data for regression problems, or creating sets for issues such as machine translation, question answering or text summarization.Additional features include pre-annotation, active sampling, online learning and real-time inter-annotator agreement.The system is integrated with the popular messaging platform: Facebook Messanger.Usability experiment showed the advantages of the proposed platform compared to other labelling tools.
Segmentation of Satellite Imagery of Amedi Site Using Chan–Vese Model with Saliency Estimation

The images that have been taken from space satellites are described by satellite imagery. The presence of the earth's surface is detected by remote sensing. Normally the source of the satellite image is barely seen, because many points in the sky are obscured with cloud shadows. Therefore, one of the most important and ubiquitous tasks in image analysis is segmentation. Segmentation is the method of dividing a image into a collection of specific regions that vary in some essential qualitative or quantitative manner. In this paper we will focus on a method for segmenting images that was developed Three different methods to detect the location of the satellite images have been studied, implemented, and tested; these are based on Chan-Vese and saliency map segmentation, and multi-resolution segmentation to obtain a proper object segmentation. In this study, the combination of the proposed segmentation automatic detection and image enhancement technique has been performed to reduce the noise of the original image. In addition, the Bilateral filter, and histogram equalization are used in these proposed techniques. Experimental results demonstrate that the suggested method can precisely extract the objective of Amedi site from the satellite images with difficult backgrounds and overlapping regions.
DATA INTEGRITY METHOD FOR DYNAMIC AUDITING IN CLOUD ENVIRONMENT

The cloud storage advantages make more people to outsource the data in the cloud.Many sensitive information like hospital, banks, private and public organization information and many IOT information like traffic, sensor information are stored in the cloud and for accurate analysis there must be a guaranteed integrity.Unlike a local storage user have not a control in the cloud environment data and the user in the cloud are concerned about data integrity.In the existing auditing scheme have several problems like low efficiency at dynamic auditing, leakage of information and huge auditing cost.To overcome these difficulties a dynamic integrity auditing is proposed for privacy protection in cloud.Existing work allows the data integrity is verified without the control of the original data file.The verification that will done by trusted third party called as data auditing and the auditor is the third party.However, such methods have several drawbacks.A necessary authentication/authorization is miss between the cloud server and auditor and 'auditing-as-a-service' is at risk.The recent methods are based on BLS signature which supports full dynamic data update in fixed size data and supports only fixed size data which is called as coarse-grained updates.So, for each small updates the updating and recomputation of authenticator is needed for all the file block.It causes higher communication and storage overhead.In this paper a fine-grained update and fully supported authorized auditing is supported which reduces communication overheads while verifying small updates.The experimental result shows that the proposed scheme provides flexibility, security and lower overhead for a greater number of regular small updates which is in business transactions and social media.
The property of being a model complete theory is preserved by Cartesian extensions

Cartesian-quotient extensions of theories constitute a most common class of nitary transformation methods for rst-order combinatorics.In this paper, some technical properties of classes of algebraic Cartesian and algebraic Cartesian-quotient interpretations of theories are studied.It is established that any algebraic Cartesian interpretation preserves the property of being a model complete theory; besides, an example of an algebraic Cartesian-quotient interpretation of theories is given, which does not preserve the model-completeness property.
A Novel AES Cryptographic Core Highly Resistant to Differential Power Analysis Attacks

In this work, we present a novel core implementation of the Advanced Encryption Standard with an integrated countermeasure against side channel attacks, which can theoretically increase the complexity of a DPA attack by a factor of 240. This countermeasure is based on mathematical properties of the Rijndael algorithm, and retains compatibility with the published Standard. The entire system was designed from the ground up to allow the reutilization of the building blocks in many different combinations, thus providing for design space exploration. Synthesis results show that the protected core can perfectly meet the performance constraints of currently used smart cards.
Clustered Echo State Networks for Signal Observation and Frequency Filtering

Echo State Networks (ESNs) are recurrent neural networks that map an input signal to a high-dimensional dynamical system, called reservoir, and possess adaptive output weights. The output weights are trained such that the ESN’s output signal fits the desired target signal. Classical reservoirs are sparse and randomly connected networks. In this article, we investigate the effect of different network topologies on the performance of ESNs. Specifically, we use two types of networks to construct clustered reservoirs of ESN: the clustered Erdös–Rényi and the clustered Barabási-Albert network model. Moreover, we compare the performance of these clustered ESNs (CESNs) and classical ESNs with the random reservoir by employing them to two different tasks: frequency filtering and the reconstruction of chaotic signals. By using a clustered topology, one can achieve a significant increase in the ESN’s performance.
Data-Driven Parameter Choice for Illumination Artifact Correction of Digital Images

We propose a new procedure for image illumination correction with data-driven parameter choice. This procedure aims at estimating the reflectance image from a corrupted version in which the corruption is due to pointwise multiplicative illumination artifact. The log -illumination artefact consists of “smooth” variations of the intensity which are modelled by a function lying in a finite dimensional space. Then a -y-correction is incorporated. The question of model selection is difficult to solve. We propose an entropy minimization criterion for the selection of both the approximating log -illumination space dimension and the -y-coefficient, so that no parameter tuning is needed. Several experiments are presented using this approach. A comparison to other methods illustrates the relevance of this approach.
Aplikasi Zoom pada Pelatihan Kepemimpinan Administrator

The pandemic forced all training activities to become disrupted, the way that is commonly done with zoom application technology. The purpose of this study is to provide an argument that occurs in an administrator leadership training in 2020 during a pandemic with a zoom facility. A descriptive qualitative method with questionnaires to participants with a google form, and analyzed with a google sheet, analyzing the results of interviews and participatory observation. Based on the results of the analysis, it is known that the zoom-in participants who are in the category x generation experiences boredom, and claims that the ideal zoom time of 1-3 hours is 69.7%. And as many as 31.3% claims to be more than 3 hours, with the zoom condition is not monotonous. The results of this study are only limited to the operational aspects of zoom, there are other possible aspects to provide better outcomes for Administrator Leadership Training.
Efficient lattice‐based authenticated key exchange based on key encapsulation mechanism and signature

Authenticated key exchange protocol is widely applied in the Internet services. Lattice-based key exchange protocols turn out to be quantum-resistant and hence have attracted tremendous attention. A lattice-based explicit authenticated key exchange protocol is constructed by combining an IND-CPA key encapsulation mechanism with a strong EUF-CMA digital signature under the message-recovery mode. Concrete parameter specifications are suggested under the 102 and 218 bits post-quantum security, respectively. Compared with the implicit authenticated key exchange derived directly from the key encapsulation mechanism, our proposals reduce the communication costs by 21.7% and 25.7%, respectively, under the same post-quantum security level. Rough analysis shows that the proposals also obtain some advantages over previous constructions in term of computational efficiency. Moreover, our scheme achieves perfect forward secrecy while the original scheme only satisfies weak forward secrecy.
DSL Approach to Deep Learning Lifecycle Data Management

A new approach to Deep Learning (DL) lifecycle data management tool support is presented: a very simple DL lifecycle data management tool, which however is usable in practice (it will be called Core tool) and a very advanced extension mechanism for this Core tool which in fact converts the Core tool into a DSL tool building framework for DL lifecycle data management tasks.The extension mechanism is based on the metamodel specialisation approach to Domain Specific Language (DSL) modelling tools introduced by the authors.The main idea of metamodel specialisation is that we first define the Universal Metamodel (UMM) for a domain and then for each use case in the domain define a Specialised Metamodel (SMM).The paper concludes with a detailed description of future research directions, concerned with defining a more general UMM and its usage.
Evaluation of Post-hoc Explanations for Malaria Detection

It has been advocated that post-hoc explanation techniques are crucial for increasing the trust in complex Machine Learning (ML) models. However, it is so far not well understood whether such explanation techniques are useful or easy for users to understand. In this work, we explore the extent to which SHAP’s explanations, a state-of-the-art post-hoc explainer, help humans to make better decisions. In the malaria classification scenario, we have designed an experiment with 120 volunteers to understand whether humans, starting with zero knowledge about the classification mechanism, could replicate the complex ML classifier’s performance after having access to the model explanations. Our results show that this is indeed the case, i.e., when presented with the ML model outcomes and the explanations, humans can improve their classification performance, indicating that they understood how the ML model makes its decisions.
PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL Generation

Text-to-SQL is a task of translating utterances to SQL queries, and most existing neural approaches of text-to-SQL focus on the cross-domain context-independent generation task.We pay close attention to the cross-domain context-dependent text-to-SQL generation task, which requires a model to depend on the interaction history and current utterance to generate SQL query.In this paper, we present an encoder-decoder model called PG-GSQL based on the interaction-level encoder and with two effective innovations in decoder to solve cross-domain context-dependent text-to-SQL task. 1) To effectively capture historical information of SQL query and reuse the previous SQL query tokens, we use a hybrid pointer-generator network as decoder to copy tokens from the previous SQL query via pointer, the generator part is utilized to generate new tokens.2) We propose a guide component to limit the prediction space of vocabulary for avoiding table-column dependency and foreign key dependency errors during decoding phase.In addition, we design a column-table linking mechanism to improve the prediction accuracy of tables.On the challenging cross-domain context-dependent text-to-SQL benchmark SParC, PG-GSQL achieves 34.0% question matching accuracy and 19.0% interaction matching accuracy on the dev set.With BERT augmentation, PG-GSQL obtains 53.1% question matching accuracy and 34.7% interaction matching accuracy on the dev set, outperforms the previous stateof-the-art model by 5.9% question matching accuracy and 5.2% interaction matching accuracy.
An Information-Theoretic Framework for Unifying Active Learning Problems

This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant. We first introduce a novel active learning criterion that subsumes an existing LSE algorithm and achieves state-of-the-art performance in LSE problems with a continuous input domain. Then, by exploiting the relationship between LSE and BO, we design a competitive information-theoretic acquisition function for BO that has interesting connections to upper confidence bound and max-value entropy search (MES). The latter connection reveals a drawback of MES which has important implications on not only MES but also on other MES-based acquisition functions. Finally, our unifying information-theoretic framework can be applied to solve a generalized problem of LSE and BO involving multiple level sets in a data-efficient manner. We empirically evaluate the performance of our proposed algorithms using synthetic benchmark functions, a real-world dataset, and in hyperparameter tuning of machine learning models.
A Novel Method to Denoise Images Based on a Meta-heuristic Algorithm and Pre-learned Dictionary

Noise may affect images in many ways during different processes. Such as during obtaining, distribution, processing, or compressing. The Sparse Representation (SR) algorithm is one of the best strategies for noise reduction. One meta-heuristic algorithm is the Particle Swarm Optimization (PSO). This research demonstrates excellent results in noise reduction in the Fast PSO version while utilizing the SRs as well as meta-heuristic algorithms to gain. This method is known as FPSO-MP and it depends on the Pursuit Algorithm (MP) that matches. In this proposed study, a Dynamic-Multi-Swarm (DMS) method and a pre-learned dictionary (FPSO-MP) approach is presented to reduce the time for the learning dictionary calculations. The output of the denoising algorithm QPSO-MP is dependable on dictionary learning because of the dictionary size or increased number of patches. Similar to this work, a Non-locally Estimated Sparse Coefficient (NESC) is one explanation for the low efficiency of the original algorithm. Compared to the original PSO-MP method, these enhancements have achieved substantial gains in computational efficiency of approximately 92% without sacrosanct image quality. After modification, the proposed FPSO-MP technique is in contrast with the original PSO-MP method. The scientific findings demonstrate that the FPSO-MP algorithm is much more efficient and faster than the original algorithm, without affecting image quality. The proposed method follows the original technique and therefore reduces during run-time. The result of this study demonstrates that the bestdenoised images can always be accessed from the pre-learned dictionary rather than the learning dictionary developed across the noisy image during runtime. We constructed images dataset from the BSD500 collection and performed a statistical test on these images. The actual findings reveal that the suggested method is excellent for noise reduction (noise elimination) as well as highly efficient during runtime. The analytical findings indicate that both quantitative and image performance outcomes are obtained with the proposed FPSO-MP approach during its contradiction with when denoising algorithms.