Reviewerly/exHarmony huggingface dataset:
Analysis of the Essence of the Concepts of “Competence” and “Competency” in the System of Psychological and Pedagogical Categories

The importance of the topic of the article is that nowadays there is a need to update methods and techniques of teaching in the educational process, the use of innovations in student teaching, because it is not important how much knowledge a student receives, but how one can use them in practice. The purpose of the article is the need to study and justify the importance of studying the essence of the concepts of “the competence” and “the competency” in the system of psychological and pedagogical categories. The article gives a theoretical justification for the concepts of “competence” and “competency” of the teacher in the pedagogical context; the similarity and difference of the concepts “the competence” and “the competency” in the system of psychological and pedagogical categories are investigated; the concept of professional competency and its categorical essence as a set of professional knowledge, skills, abilities necessary for both teachers and students in the XXI century is given. Nowadays, the purpose of school education is determined by the entirety of knowledge, skills and abilities that a student shouldacquire. In the XXI century, such a perspective is insufficient for society, because professional educational institutions, industry, families do not need weisenheimers, but graduates who are ready tointegrate in the real life, who have the ability to quickly and easily approach life and professional problems, which appear before them, so that getting into a problem situation, they could find several ways to solve it, to choose a practical way, justifying one’s decision.
Influence of University Professors on the Formation of Personal Maturity of Students

Psychological and pedagogical interaction in higher education is characterized by certain features, among which, the determining is the influence of the professor on the formation of students' personal maturity. Under the influence of internal (personal) and external (social) factors, meaningful personal formations arise, that characterize this phenomenon. The article explicates signs of personal maturity: at the individual level - adequate self-esteem, a high level of self-regulation, independence, and responsibility; within social interaction - the ability of the individual to be tolerant and empathic. The process of professor-student interaction in higher education institutions should be aimed at maintaining an enthusiastic attitude toward the future profession, and the development of relevant qualities and features necessary for the future specialist, which adds relevance to the study. An effective means of influencing the formation of students' personal maturity is psychological and pedagogical support for all participants of the educational process in higher education institutions, which corresponds to the natural concept of personality development in the dialectical unity of postmodern approaches to understanding the psyche. The result of such support lies in the preservation of moral, spiritual, and mental health, the promotion of emotional well-being of students, the development of natural potential, and the activation of internal resources that ensure the aspiration of a young person to self-development. The article outlines the peculiarities of the influence of university professors on the formation of students’ personal maturity. The latter is activated due to the professors’ readiness to create conditions for psychological and pedagogical support and effective organization of the education process. The prospects for further research lie in outlining methods for the development and formation of students' personal maturity within the process of professor-student interaction during distance learning and the subsequent testing of their effectiveness in the educational process.
The Role of the Integrated Image of the Problem Solving Method in the Realization of the Mathematics Teaching Continuity

In the article we investigated the problem of forming in students' minds an integrated image of the way of solving mathematical problems. There were considered the possibilities of realization of intrasubject integration in the process of studying math. The methodical conditions under which due to the integrated image of the method of solving the problem the realization of the continuity of teaching mathematical disciplines during the transition from school to university was revealed. We have chosen the method of completing the square to describe the authors' methods of teaching math. Curricula and school textbooks currently in force in Ukraine were analyzed. We also considered universities' curricula of Mathematical Analysis. An experimental study with students on the formation of an integrated image of the method of solving problems, which showed positive dynamics and proved the effectiveness of the developed methodology. It was concluded that the formation of an integrated image of the method of solving problems during a long study of mathematics was possible with the correct choice of problems that are solved in a given way; application of the system of didactic principles, in particular systematicity, consistency and perspective; constant generalization and systematization of knowledge, skills and abilities of students.
Measuring Non-Probabilistic Uncertainty: A cognitive, logical and computational assessment of known and unknown unknowns

There are two reasons why uncertainty may not be adequately described by Probability Theory. The first one is due to unique or nearly-unique events, that either never realized or occurred too seldom for frequencies to be reliably measured. The second one arises when one fears that something may happen, that one is not even able to figure out, e.g., if one asks: "Climate change, financial crises, pandemic, war, what next?" In both cases, simple one-to-one cognitive maps between available alternatives and possible consequences eventually melt down. However, such destructions reflect into the changing narratives of business executives, employees and other stakeholders in specific, identifiable and differential ways. In particular, texts such as consultants' reports or letters to shareholders can be analysed in order to detect the impact of both sorts of uncertainty onto the causal relations that normally guide decision-making. We propose structural measures of cognitive maps as a means to measure non-probabilistic uncertainty, eventually suggesting that automated text analysis can greatly augment the possibilities offered by these techniques. Prospective applications may concern actors ranging from statistical institutes to businesses as well as the general public.
One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers

Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. It can save the cost of manually labeling data in real-world applications such as robot vision and autonomous driving. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. However, such an assumption does not always hold in practice owing to the collection difficulty and the scarcity of the data. Thus, we aim to relieve this need on a large number of real data, and explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization (OSDG) problem, where only one real-world data sample is available. To remedy the limited real data knowledge, we first construct the pseudo-target domain by stylizing the simulated data with the one-shot real data. To mitigate the sim-to-real domain gap on both the style and spatial structure level and facilitate the sim-to-real adaptation, we further propose to use class-aware cross-domain transformers with an intermediate domain randomization strategy to extract the domain-invariant knowledge, from both the simulated and pseudo-target data. We demonstrate the effectiveness of our approach for OSUDA and OSDG on different benchmarks, outperforming the state-of-the-art methods by a large margin, 10.87, 9.59, 13.05 and 15.91 mIoU on GTA, SYNTHIA$\rightarrow$Cityscapes, Foggy Cityscapes, respectively.
MOBILE PHONE RECOMMENDER USING MULTI CRITERIA DECISION MAKING ALGORITHM

A study found that the depression rate is growing at an alarming rate among everyone. Many people who report symptoms of depression mostly have not been diagnosed or underwent treatments for it. If they do not get proper treatments like medication, therapy, guidance or counselling, it would be difficult for them to lead a happy and stress-free lifestyle. India is already on the cusp of a health crisis, and we urgently require a long-term solution to the problem of depression. People are more inclined to open it up to a smart machine than to a human, according to a recent study. Digital interfaces are gaining traction as feasible options for closing the gap and making mental diagnosis and treatment more accessible and inexpensive to everybody. The aim of the project is to develop a chatbot called Therapy Bot using sentiment analysis and cognitive behavioral therapy to predict the mental health status of an individual. Moreover, the chatbot can serve as a good companion to the affected by communicating with friendly manner and help them recover. The chatbot will personalize its responses based on the user's answer to keep the conversation interesting. The chatbots can be used as a complement to treatment or as a kind of interim support while waiting for an appointment. The benefit of such a method is that, rather than reaching a point where a trip to a psychologist is required, an online free version will reach a large number of people, mitigate the negative effects of depression, and contribute to a better of society.
FEATURE ENGINEERING WITH SENTENCE SIMILARITY USING THE LONGEST COMMON SUBSEQUENCE FOR EMAIL CLASSIFICATION

Feature selection plays a prominent role in email classification since selecting the most relevant features enhances the accuracy and performance of the learning classifier. Due to the exponential increase rate in the usage of emails, the classification of such emails posed a fitting problem. Therefore, there is a requirement for a proper classification system. Such an email classification system requires an efficient feature selection method for the accurate classification of the most relevant features. This paper proposes a novel feature selection method for sentence similarity using the longest common subsequence for email classification. The proposed feature selection method works in two main phases: First, it builds the longest common subsequence vector of features by comparing each email with all other emails in the dataset. Later, a template is constructed for each class using the closest features of emails of a particular class. Further, email classification is tested for unseen emails using these templates. The performance of the proposed method is compared with traditional feature selection methods such as TF-IDF, Information Gain, Chi-square, and semantic approach. The experimental results showed that the proposed method performed well with 96.61% accuracy.
Youths' Perception of Digital Security Act 2018: A Study on the Youth of Bangladesh

Freedom of speech is one of the most important fundamental rights ensured by the constitution of the People's Republic of Bangladesh. However, when this right is abused, it destroys the peace and harmony of the country. But without any surveillance, digital platforms can turn into places of controversy which may trigger mass people into communal violence and riots. Thus, the government of Bangladesh has introduced Digital Security Act 2018 to secure the responsible use of the Internet and digital platforms. In this study, the researchers attempted to take a look at the perception of Bangladeshi youth on the Digital Security Act 2018. This study followed survey and in-depth interview methods to gather data. Researchers found out that most young people feel the digital security act is working against freedom of expression on social media. They demand viable changes to this act and agreed to the notion that responsible digital citizenship can ensure freedom of expression on social media.
Mandatory vaccination requirements and the green Covid-19 certificate: a fundamental rights perspective in Italy

Since the Covid-19 pandemic outbreak in March 2020, governments worldwide have resorted to various measures to manage and contain the spread of the virus. In Italy, for example, severe restrictions on freedom of movement were enforced from the very beginning, and they lasted until the vaccination campaign picked up in Spring 2021. At that point, the executive started to lift the most severe measures progressively, and, at the same time, it began to use other legal instruments. For instance, besides progressively enforcing a compulsory vaccination requirement for some categories of people only, the executive introduced a novelty in the Italian legal system: the Green Covid-19 Certificate (or ‘Green Pass’), which not only became a condition to access venues and make use of services, but it also became a condition for the enjoyment or exercise of fundamental rights, including the rights to education and work. The aim of this paper is to assess the Italian Government’s decisions through the lenses of liberal democratic constitutionalism, reflecting upon the balance that was struck between public health and individual fundamental rights. This kind of assessment is especially important in times of emergency, as they are typically defined by fast-tracked and temporary decisions which may, nevertheless, “inadvertently” become embedded in the constitutional legal order.
Elementary Belief Revision Operators

Abstract Discussions of the issue of iterated belief revision are commonly accompanied by the presentation of three “concrete” operators: natural, restrained and lexicographic. This raises a natural question: What is so distinctive about these three particular methods? Indeed, the common axiomatic ground for work on iterated revision, the AGM and Darwiche-Pearl postulates, leaves open a whole range of alternative proposals. In this paper, we show that it is satisfaction of an additional principle of “Independence of Irrelevant Alternatives”, inspired by the literature on Social Choice, that unites and sets apart our three “elementary” revision operators. A parallel treatment of iterated belief contraction is also given, yielding a family of elementary contraction operators that includes, besides the well-known “conservative” and “moderate” operators, a new contraction operator that is related to restrained revision.
Assessing Soft Skills for Software Requirements Engineering Processes

Software requirement engineering (SRE) is the process of establishing, documenting, and maintaining software requirements. The goal of this research is to investigate the importance of soft skills in SRE. The data collection was performed through an online questionnaire. Descriptive statistics, principal components analysis (PCA), and stepwise regression techniques were used to analyze the data. A comprehensive review determined the 31 soft skills associated with SRE. There were 122 software development experts in Gaza who participated in the survey. The PCA analysis extracted six factors, named problem-solving, learning willingness, commitment, pressure resilience, critical thinking, and interpersonal skills. The analysis discovered that the level of SRE practice in Gaza is 73.71%. Furthermore, it was determined that 89.2% of respondents have critical thinking skills, and 85% have problem-solving and commitment skills. The result shows that all soft skills factors have strong links to SRE. However, only four soft skills (problem-solving, willingness to learn, pressure tolerance, and critical thinking) were found to have an impact on SRE. Considering these findings, we recommend focusing on the development of soft skills, especially problem-solving and willingness to learn skills, for the team analyzing software system requirements.
Online learning impact on transitioning to ERT: Mexican ELT experiences

This project set out to scrutinise the experiences of a group of students pursuing an online B.A. in English Language Teaching (ELT) at four different public universities in Mexico while being in-service English teachers at different levels in public and private sectors. Their teaching practice was given in face-to-face settings; however, due to the COVID-19 pandemic, these teacher-students were required to migrate their practice to remote teaching. It is in this line of arguments that the researchers, as the participants’ ELT teachers, were interested in knowing if any strategies from the online-learning student role were useful to transition as remote ELT-teachers. As a follow-up method to questionnaires administered to 55 online ELT students, semi-structured interviews were conducted to a total of 11 participants, from the participating universities. Findings indicate that online learning experience does have a positive impact when transitioning from face-to-face instruction to remote teaching. Regardless of the participants’ ICT skills development, there was evidence of soft skills fostering. Moreover, the challenges and limitations they faced inevitably led to a fruitful reengineering process.
Hayford’s Duplicates: Cobbling a Model of Melville’s Moby-Dick

In "Unnecessary Duplicates," renowned Melville editor Harrison Hayford discusses duplicate and vestigial settings, events, and characters in Moby-Dick, showing how they serve both wider aesthetic and structural effect in the novel.Their seemingly "unnecessary" nature prompts Hayford to offer several compositional hypotheses to explain their origins.With no extant manuscripts, Hayford relies on stylistic evidence, inductive reasoning, and Melville's personal letters to posit three primary draft stages of the novel.As with Hayford's duplicates, evidence of phenomena that produces data can be scant or absent.Multiple data sources may be patched together to paint a fuller picture of that phenomena.Model-based collaborative filtering methods can be used to characterize sparse data.Their outputs are recognizable in the recommendations of commerce and streaming websites.One such method, nonnegative matrix factorization (NMF), has proven successful in these tasks.Factorizing a table of numeric records approximates two factor matrices that potentially produced the table, thus estimating latent features not explicitly present in the data.NMF's constraint of producing matrices with nonnegative values better isolates those features and produces more human-interpretable models.This chapter demonstrates how a probabilistic variant of NMF can be used to characterize the parts of speech in the sentences of Moby-Dick.Hayford's ideas on Melville's draft stages are explored and tested via the NMF model.
The Remaining 23 Valid Aristotelian Syllogisms can be Deduced only from the Syllogism IAI-3

Syllogism reasoning is a common and important form of reasoning in natural language and logic.This paper shows that the remaining 23 valid syllogisms can be deduced merely from the syllogism IAI-3 by making the best of propositional logic and generalized quantifier theory, so as to achieve the goal of deeply discussing the reducible relations between the syllogism IAI-3 and the other syllogisms.More specifically, on the basis of formalizing syllogisms, this paper makes full of rules of deduction in classical propositional logic, the definitions of outer and inner negative quantifiers of Aristotelian quantifiers, and the symmetry of Aristotelian quantifiers no and some in generalized quantifier theory, and then establishes a concise formalized axiom system for Aristotelian syllogistic logic.This innovative research not only shows that formalized logic has the characteristics of structuralism, but also provides a concise and general mathematical paradigm for studying other syllogistic logics, and also provides theoretical support for knowledge and information processing in artificial intelligence and computer science.
On the support $t$-designs of extremal Type III and IV codes

Let $C$ be an extremal Type III or IV code and $D_{w}$ be the support design of $C$ for a weight $w$. We introduce the two numbers $\delta(C)$ and $s(C)$: $\delta(C)$ is the largest integer $t$ such that, for all weights, $D_{w}$ is a $t$-design; $s(C)$ denotes the largest integer $t$ such that there exists a $w$ such that $D_{w}$ is a $t$-design. In the present paper, we consider the possible values of $\delta(C)$ and $s(C)$.
A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks

The field of deep learning has witnessed a remarkable shift towards extremely compute- and memory-intensive neural networks. These newer larger models have enabled researchers to advance state-of-the-art tools across a variety of fields. This phenomenon has spurred the development of algorithms for distributed training of neural networks over a larger number of hardware accelerators. In this paper, we discuss and compare current state-of-the-art frameworks for large scale distributed deep learning. First, we survey current practices in distributed learning and identify the different types of parallelism used. Then, we present empirical results comparing their performance on large image and language training tasks. Additionally, we address their statistical efficiency and memory consumption behavior. Based on our results, we discuss algorithmic and implementation portions of each framework which hinder performance.
Data fusion using neural networks for intelligent multilevel control of artificial and natural objects: a survey

Abstract In the modern world, data fusion (DF) technique has been playing an increasingly important role for effective and efficient data transformation in subject domains. DF aims to solve the problem of control of artificial and natural objects. Although DF has been intensively studied, neither unified problem statements of the fusion of the heterogeneous, multimodal, complex, imperfect data nor in-depth elaboration of characteristics and challenges about the application of DF on such specific situations are available. It is also unclear to what extent DF challenges have been addressed and what the overall research landscape of DF is. We define the requirements for DF in the interests of intelligent control of artificial and natural objects and look at the applications, reveal the capabilities of traditional DF methods and analyze the advantages that neural networks (NN) have over them. We show that despite of the evident advantages, none of the existing NNs satisfy all the requirements of DF. Based on the analysis of NN architectures, we determine the requirements for advanced NN architectures and discuss future research directions.
Data-characteristic-aware Latent Feature Learning

Some state-of-the-art missing data predictors are primarily based on a latent feature learning (LFL)-based model [1–14]. They improved the basic LFL model based on the neighborhood information of historical recorded data [15–17]. While they have some limitations as follows:To address the above problems, a data-characteristic-aware latent factor (DCALF) model is proposed in [55]. Its main idea is towfold: (1) it first extracts the dense latent features from the original raw HDI data by an LFL model, and (2) it employs DPClust method [21] to simultaneously identify the neighborhoods and outliers of HDI data on the extracted latent features.
Qudit lattice surgery

We observe that lattice surgery, a model of fault-tolerant qubit computation, generalises straightforwardly to arbitrary finite-dimensional qudits. The generalised model is based on the group algebras $\mathbb{C}\mathbb{Z}_d$ for $d \geq 2$. It still requires magic state injection for universal quantum computation. We relate the model to the ZX-calculus, a diagrammatic language based on Hopf-Frobenius algebras.