Reviewerly/exHarmony huggingface dataset:
Whose offence is it anyway?

On December 16, 2012, a 23 year-old girl was brutally gang-raped by six men, including one minor, on a moving bus while her male friend was assaulted in Delhi. Both of the victims were attacked with an iron rod and the girl was severely injured and later died. The media was quick to hype cringe-worthy comments made by people in the limelight. The discourse around status of women in the society, patriarchal norms, gender sensitivity and umpteen related constructs became the content of comment threads on various web pages. The articulation of these discourses on a digital medium is both conflicting and intriguing. This paper will attempt to conduct the discourse analysis of the comment threads on few websites that posted updates around this mishap. Since the online medium gives people the advantage of anonymity which may not otherwise be available to them in the public sphere, the constructs of identity and ideology become even more critical. This research paper will attempt to identify the dominant discourses, analyse their ideological context and the potential and the role of digital media in these constructions. Does the digital media ecosystem reinforce the hegemonic ideologies or does it provide the space for liberal and alternative ideologies? Do perceptions of gender and sexuality assume fluid meanings or get re-negotiated in the digital media context? This research will attempt to use the discourse analysis of the web comment threads around the brutal mishap to discuss and analyse these questions.
Efficient methods for Gaussian Markov random fields under sparse linear constraints

Methods for inference and simulation of linearly constrained Gaussian Markov Random Fields (GMRF) are computationally prohibitive when the number of constraints is large. In some cases, such as for intrinsic GMRFs, they may even be unfeasible. We propose a new class of methods to overcome these challenges in the common case of sparse constraints, where one has a large number of constraints and each only involves a few elements. Our methods rely on a basis transformation into blocks of constrained versus non-constrained subspaces, and we show that the methods greatly outperform existing alternatives in terms of computational cost. By combining the proposed methods with the stochastic partial differential equation approach for Gaussian random fields, we also show how to formulate Gaussian process regression with linear constraints in a GMRF setting to reduce computational cost. This is illustrated in two applications with simulated data.
Utilización de aplicaciones web para optimizar el proceso de enseñanza aprendizaje: Use of web applications to optimize the teaching-learning process

espanolEl presente trabajo que se llevo a cabo es acerca de la utilizacion de aplicaciones web para optimizar el proceso de ensenanza y aprendizaje de los estudiantes. Las aplicaciones web permiten una vinculacion entre el usuario y la informacion, mediante esto el usuario puede acceder a datos de una forma interactiva y asimetrica, ademas, estas presentan ciertas ventajas tales como el tiempo, espacio, y una mejor relacion entre los docentes y estudiantes. EnglishThe present work that was carried out is about the use of web applications to optimize the teaching and learning process of students. Web applications allow a link between the user and the information, through this the user can access data in an interactive and asymmetric way, in addition, it has certain advantages such as time, space, and a better relationship between teachers and students.
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation

For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user’s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method employs Bayesian optimisation to focus the user’s labelling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multi-document summarisation, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarisation.
Enhancing Playout Policy Adaptation for General Game Playing

Playout Policy Adaptation (PPA) is a state-of-the-art strategy that has been proposed to control the playouts in Monte-Carlo Tree Search (MCTS). PPA has been successfully applied to many two-player, sequential-move games. This paper further evaluates this strategy in General Game Playing (GGP) by first reformulating it for simultaneous-move games. Next, it presents five enhancements for the strategy, four of which have been previously successfully applied to a related MCTS playout strategy, the Move-Average Sampling Technique (MAST). Experiments on a heterogeneous set of games show three enhancements to have a positive effect on PPA: (i) updating the policy for all players proportionally to their payoffs instead of updating only the policy of the winner, (ii) collecting statistics for N-grams of moves instead of single moves only, and (iii) discounting the backpropagated payoffs depending on the depth of the playout. Results also show enhanced PPA variants to be competitive with MAST for small search budgets, and better for larger search budgets. The use of an \(\epsilon \)-greedy selection of moves and of after-move decay of statistics, instead, seem to have a detrimental effect on PPA.
ORGANIZATIONAL AND PEDAGOGICAL SUPPORT OF THE PROCESS OF DEVELOPMENT OF LEADERSHIP COMPETENCE OF FUTURE HIGHER SCHOOL TEACHERS

Objective. The theoretical analysis of the problem of searching for organizational and pedagogical conditions for the development of leadership competence of future higher school teachers and ways to implement them in the educational process in HEIs was conducted in the article. Methods. To gain the objective, the following theoretical research methods were used: analysis, synthesis, comparison, generalization of scientific sources on the research problem to clarify the essence of the concept of “organizational and pedagogical support”, determination of organizational and pedagogical conditions of leadership development of future higher school teachers in the educational environment of HEIs. Results. The scientific research found that the reference point of successful improvement of organizational and pedagogical support of the process of development of leadership competence of future higher school teachers in the educational environment of HEIs is organizational and pedagogical conditions during training of holders of a master’s degree of the educational program “Higher School Pedagogy” in their complex combination: 1) training of scientific and pedagogical staff of HEIs for the process of development of leadership competence of future higher school teachers; 2) use of the didactic potential of the distance learning system in HEIs; 3) introduction of the Kaizen system in the process of training holders of a master’s degree of the educational program “Higher School Pedagogy”. Conclusions. The ways of realization of organizational and pedagogical conditions were demonstrated. Training of scientific and pedagogical staff of HEIs for the process of development of leadership competence of future higher school teachers should be implemented through the introduction of the author’s curriculum of the course “Innovative teaching methods in higher education: the latest ones in European and world practice”; increasing the teachers’ participation in scientific and practical conferences of different levels, round tables, master classes, trainings, etc. It is expedient to use the didactic potential of the distance learning system in HEIs by creating a distance course “Fundamentals of Leadership”, its functionality and didactic tools and active implementation during the distance educational process. The introduction of the Kaizen system in the process of training holders of a master’s degree of the educational program “Higher School Pedagogy” should be implemented through a permanent workshop “Kaizen Technology: the Path to Leadership”, the university’s development strategy, the university’s mission “Together to the heights of professional excellence and life success of everyone for the prosperity of Ukraine”
EDUCATIONAL-METHODICAL COMPLEX OF THE DISCIPLINE AS A MEANS OF DEVELOPING SELF-EDUCATIONAL ACTIVITIES OF STUDENTS

The article presents a list of educational-and-methodological materials for the students’ self-education activity developing; main recommendations and requirements for educational-and-methodological complex and its electronic version creating are considered as well.
The Logical Character of the Principle of Induction 1

Induction is essentially different from deductive inference. It can never attain certainty. All attempts to transform inductive into deductive inference fail because they necessitate the introduction of inductive premises. The unsatisfactory state of the problem of induction seems to the author to be due to the preoccupation with the problem of the validity of induction. The more fundamental question concerning the meaning of the principle of induction is rather neglected and it is this which needs a careful, logical analysis. The “indifference” or “irrelevancy,” which is the crucial concept in the principle, means generally causal irrelevance. Causal irrelevance, however, is identical with random distribution, and random distribution is a fact which can be established only by statistical investigation. The probability of induction is therefore established on the basis of generalizations for which there is no probability at all.
Can NLI Models Verify QA Systems’ Predictions?

To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just “good enough” in the context of imperfect QA datasets. We explore the use of natural language inference (NLI) as a way to achieve this goal, as NLI inherently requires the premise (document context) to contain all necessary information to support the hypothesis (proposed answer to the question). We leverage large pre-trained models and recent prior datasets to construct powerful question conversion and decontextualization modules, which can reformulate QA instances as premise-hypothesis pairs with very high reliability. Then, by combining standard NLI datasets with NLI examples automatically derived from QA training data, we can train NLI models to evaluate QA models’ proposed answers. We show that our approach improves the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. Careful manual analysis over the predictions of our NLI model shows that it can further identify cases where the QA model produces the right answer for the wrong reason, i.e., when the answer sentence cannot address all aspects of the question.
ЦИФРОВА ІДЕНТИЧНІСТЬ ЯК ВІДОБРАЖЕННЯ ІНФОРМАЦІЙНОГО СУСПІЛЬСТВА

Usanova L. A., Suprun H. H. Digital identity as areflection of information society. – Article.The article investigates issues of digital identityas a response to challenges of the contemporaryinformation society. The analysis of individualizationand self-presentation processes of an individual on theinternet is conducted. With the spread of the role ofinternet identity as a modern phenomenon, personalsocial presentation becomes an important condition ofonline and offline life. The article highlights specificaspects and methods of digital image creation throughinterpersonal communicative activity. «Slacktivism»and «post-truth» phenomena of the digital environmentare analyzed. Slacktivism is a common product ofdigital field development and it is a way of reactingto societal tendencies due to the wish of building selfesteemas an individual that responds to challenges andtrends of the present time. The phenomenon of posttruthis considered as information (a statement) withmultifaceted interpretation possibilities without claimingobjectivity. The consequence of that is manipulation andmisinformation in the information field. It is pointedout that verified and objective facts combine or evengive way to messages that are aimed directly at theemotional reaction of both individuals and broad socialgroups as a whole. The article also emphasizes that atthe beginning of its development, the Internet servedmostly as an information and educational platform, butwith the spread and availability of digital technologies itwas transformed into a social and communicative space.Emphasis is placed on the fact that the phenomenon ofdigital identity is carried out through interpersonalcommunication and self-representation in the digitalenvironment. Individualization of personality becomesdynamic in line with new variables that may ariseboth in the information field and within interpersonalcommunication. Social networks are the platforms thatgive users the opportunity for personal realization andgenerate a deceptive sense of involvement in significantprocesses on a mass scale.
Medical Dead-ends and Learning to Identify High-Risk States and Treatments

Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning. In data-constrained offline settings, both approaches may fail as they assume fully optimal behavior or rely on exploring alternatives that may not exist. We introduce an inherently different approach that identifies possible ``dead-ends'' of a state space. We focus on the condition of patients in the intensive care unit, where a ``medical dead-end'' indicates that a patient will expire, regardless of all potential future treatment sequences. We postulate ``treatment security'' as avoiding treatments with probability proportional to their chance of leading to dead-ends, present a formal proof, and frame discovery as an RL problem. We then train three independent deep neural models for automated state construction, dead-end discovery and confirmation. Our empirical results discover that dead-ends exist in real clinical data among septic patients, and further reveal gaps between secure treatments and those that were administered.
PENGARUH KECERDASAN EMOSIONAL TERHADAP PRESTASI BELAJAR PESERTA DIDIK

This study aims to determine the influence of emotional intelligence on the learning achievements of students in MAN 3 Tasikmalaya. The method used in this study is a descriptive method through a quantitative approach, with a population of all MAN 3 Tasikmalaya students and sampling using the area (cluster) sampling technique. Then the data were analyzed using non-parametric statistical types with Spearman Rank correlation. From this study, information was obtained that emotional intelligence has a positive and significant influence on student learning achievement in MAN 3 Tasikmalaya with low classification based on the calculation of the rank Spearman correlation that obtains rs = 0.40 is at the correlation interval0.21-0.40. The influence of emotional intelligence on students' learning achievement at MAN 3 Tasikmalaya has contribution of 16%, the remaining 84% is influenced by other factors namely physiological factors, psychological factors, and environmental influences.
ВЛИЯНИЕ ПАНДЕМИИ COVID-19 НА САМОЧУВСТВИЕ СТУДЕНЧЕСКОЙ МОЛОДЕЖИ В КАЗАХСТАНЕ

in Kazakhstan. Within the framework of the Abylai Khan KazUIR & WL research project Transformation of the values ​​of the Kazakh system of higher education in a multicultural and globalizing world, implemented with the support of the Ministry of Education and Science of the Republic of Kazakhstan, a sociological survey was conducted that covered 1000 students from different regions of Kazakhstan. The research was conducted online using the Google Forms application. Based on the analysis of the sociological survey, general satisfaction with the quality of higher education in the Republic of Kazakhstan, the transition to distance education, the main educational values ​​of students, trends and prospects of higher education in the Republic of Kazakhstan are shown. In an environment of instability and spreading disease, stress and anxiety levels have been shown to increase in young people around the world. Kazakhstan is no exception: every fifth student in Kazakhstan experiences predominantly negative feelings (depression, feelings of imbalance, various fears), which affects not only their well-being, but also the value picture of the world and young people's ideas about the future. At the same time, the article suggests that the pandemic is not a source of problems, but a catalyst that exacerbated the problems already existing in higher education. We are talking about the absence of the formation of values, the loss of spirituality, commercialization and consumerization of education. This study will complement the understanding of education within the framework of higher education, and the results will be useful to teachers of higher educational institutions, educational philosophers and everyone who is interested in the problems of higher education.
Falta de identificación del autor en causas generadas por el Sistema Cybertipline

In 2013, the Public Ministry of Paraguay signed an agreement with the non-governmentalentity with the National Center for Missing and Exploited Children of the United States, from nowon NCMEC. The agreement consists of the cooperation between both institutions, by NCMECgiving access to a VPN to its Cybertipline system that has reports of cases of pornography relatedto children and adolescents reported by the electronic service provider in which the allegedperpetrator has used an IP address whose geolocation corresponds to an internet provider companythat establishes residence in our country and the investigation of each case by the Paraguayanprosecutor’s office. The general objective of this research work is to establish the obstaclesthat exist in the investigation of cases of child pornography reported through the Cybertiplinesystem. The specific objectives are; determine the average number of causes that enter through theCybertipline system per year between the years 2014, 2015 and 2016, fix the difficulties to identifythe perpetrators of the causes reported in the mentioned period of time and identify measuresassigned or adopted in the Cases of child pornography reported through the Cybertipline system in which the perpetrator cannot be determined.
ФИЗИКА ФАНИ МАШҒУЛОТЛАРИДА АХБОРОТ ТЕХНОЛОГИЯЛАРИДАН ФОЙДАЛАНИШ

Use of the multimedia manual in educational process develops cognitive activity of students, allows to make the teaching process more accessible and interesting for learners.
Artificial intelligence experience

In this chapter, I discuss my art and research practice in relation to issues around artificial intelligence (AI) and the possibility of conceptualizing AI experience. By “experience”, I mean the speculative possibility of how an AI may experience either the world or their own functionality. This possibility has the potential to tap into imaginaries around AI, acting as a form of research that can help human participants reflect back on their sociotechnical practices and offer a generative route to new ontological understandings. I frame this as “poetic methods”, a term I use to describe the work I conduct at the overlap of art and research. I give an account of these methods, drawing on techniques from critical making and speculative design. I then detail how I used these techniques in a participatory workshop and subsequent installation at the Zaratan Gallery in Lisbon, Portugal. I describe the themes that emerge from the workshop and how I worked with and against them in constructing the final installation. I conclude by discussing the successes and limitations of the techniques, what they can do to thicken and enrich imaginaries and research framings around AI and some suggestions for their transferability to colleagues’ work.
The Financial Narrative Summarisation Shared Task FNS 2021:FNS 2021

This paper presents the results and findings of the Financial Narrative Summarisation Shared Task on summarising UK annual reports. The shared task was organised as part of the Financial Narrative Processing 2021 Workshop (FNP 2021 Workshop). The shared task included one main task which is the use of either abstractive or extractive automatic summarisers to summarise long documents in terms of UK financial annual reports. This shared task is the second to target financial documents. The data for the shared task was created and collected from publicly available UK annual reports published by firms listed on the London Stock Exchange. A total number of 10 systems from 5 different teams participated in the shared task. In addition, we had two baseline and two topline summarisers to help evaluate the results of the participating teams and compare them to the state-of-the-art systems.
PRIDE: Predicting Relationships in Conversations

Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers’ relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style.Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation

Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts. Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. This paper proposes a novel data augmentation technique that leverages large-scale language models to generate realistic text samples from a mixture of real samples. We also propose utilizing soft-labels predicted by the language models, effectively distilling knowledge from the large-scale language models and creating textual perturbations simultaneously. We perform data augmentation experiments on diverse classification tasks and show that our method hugely outperforms existing text augmentation methods. We also conduct experiments on our newly proposed benchmark to show that the augmentation effect is not only attributed to memorization. Further ablation studies and a qualitative analysis provide more insights into our approach.