Reviewerly/exHarmony huggingface dataset:
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation

Having engaging and informative conversations with users is the utmost goal for opendomain conversational systems.Recent advances in transformer-based language models and their applications to dialogue systems have succeeded in generating fluent and human-like responses.However, those systems still lack control over the generation process toward producing contentful responses and achieving engaging conversations.To address this, we present DiSCoL (Dialogue Systems through Coversational Line guided response generation).DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly convlines) as controllable and informative content-planning elements to guide the generation model in producing engaging and informative responses.Two primary modules in DiSCoL's pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines.Users can also change the returned convlines to control the direction of the conversations toward topics that are more interesting for them.Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.
BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification

Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis.Therefore, prediction accuracy is an important criteria which also necessitates robust predictive language models.However, the models using deep learning have been proven vulnerable towards insignificantly perturbed input instances which are less likely to be misclassified by humans.Recent efforts of generating adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the everincreasing biomedical literature poses unique challenges.We propose BBAEG (Biomedical BERT-based Adversarial Example Generation), a black-box attack algorithm for biomedical text classification, leveraging the strengths of both domain-specific synonym replacement for biomedical named entities and BERT-MLM predictions, spelling variation and number replacement.Through automatic and human evaluation on two datasets, we demonstrate that BBAEG performs stronger attack with better language fluency, semantic coherence as compared to prior work.
Human-AI interactions through a Gricean lens

Grice's Cooperative Principle (1975) describes the implicit maxims that guide conversation between humans. As humans begin to interact with non-human dialogue systems more frequently and in a broader scope, an important question emerges: what principles govern those interactions? The present study addresses this question by evaluating human-AI interactions using Grice's four maxims; we demonstrate that humans do, indeed, apply these maxims to interactions with AI, even making explicit references to the AI's performance through a Gricean lens. Twenty-three participants interacted with an American English-speaking Alexa and rated and discussed their experience with an in-lab researcher. Researchers then reviewed each exchange, identifying those that might relate to Grice's maxims: Quantity, Quality, Manner, and Relevance. Many instances of explicit user frustration stemmed from violations of Grice's maxims. Quantity violations were noted for too little but not too much information, while Quality violations were rare, indicating trust in Alexa's responses. Manner violations focused on speed and humanness. Relevance violations were the most frequent, and they appear to be the most frustrating. While the maxims help describe many of the issues participants encountered, other issues do not fit neatly into Grice's framework. Participants were particularly averse to Alexa initiating exchanges or making unsolicited suggestions. To address this gap, we propose the addition of human Priority to describe human-AI interaction. Humans and AIs are not conversational equals, and human initiative takes priority. We suggest that the application of Grice's Cooperative Principles to human-AI interactions is beneficial both from an AI development perspective and as a tool for describing an emerging form of interaction.
GENE: Global Event Network Embedding

Current methods for event representation ignore related events in a corpus-level global context.For a deep and comprehensive understanding of complex events, we introduce a new task, Event Network Embedding, which aims to represent events by capturing the connections among events.We propose a novel framework, Global Event Network Embedding (GENE), that encodes the event network with a multi-view graph encoder while preserving the graph topology and node semantics.The graph encoder is trained by minimizing both structural and semantic losses.We develop a new series of structured probing tasks, and show that our approach effectively outperforms baseline models on node typing, argument role classification, and event coreference resolution. 1
The Needs of Intelligent Information and Media Education for Students of Higher Education Institutes: The Sociological Aspect

In the article the actual modern problem that is pervasive minutes informatization of modern life, including the education system, which is not only positive but also negative effects on young people.The concept "reasonable information needs" is introduced into scientific circulation and the author's understanding of its content, essence, structure, principles, and factors is substantiated.Attention is focused on the need, in terms of the unauthorized and unlimited influence of information content, including fake and manipulative nature, on the minds of young people, including students, the introduction and improvement of media education in all institutions of higher education.Its purpose, functions, role in the formation of reasonable information needs of students of higher educational institutions of both humanitarian and technical profile of Ukraine and Poland are considered.
LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach

A LightGBM model fed with target word lexical characteristics and features obtained from word frequency lists, psychometric data and bigram association measures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data Prediction.It obtained the best performance of all teams on two of the five eye-tracking measures to predict, allowing it to rank first on the official challenge criterion and to outperform all deep-learning based systems participating in the challenge.
Transfer learning and its application research

Abstract Transfer learning relaxes the assumption’s limitation requirements of the independent and identical distribution of training data and test data in machine learning. It aims to help the target domain complete the learning task by learning one or more source domains similar to the target domain, solve the problem of scarcity of annotation data and enhance the model’s robustness and generalization performance. The article is a survey on the progress of transfer learning. According to “how to transfer”, transfer learning is divided into four categories: instance-based transfer learning, feature-based transfer learning, model-based transfer learning, and relation-based transfer learning. The paper introduces the basic assumptions, main research questions, common methods, related research of various transfer learning algorithms, and the application of the transfer learning. Finally, we try to point out future research trends.
COVID-19 - Digital Transformation and Digital Competency

The COVID-19 pandemic has caused serious and long-term disturbances in people's lives all over the world, in both developing and developed countries. This has led to financial crisis and great depression in the GDPs of many countries. It has not only impacted the Multi-National Businesses (MNCs) but also disturbed the Small and Medium Enterprises (SMEs) and life’s of man vulnerable people. As the crisis continues, the pandemic's pervasive impacts, which have affected every part of our communities, continue to unfold, leaving profound marks on people's livelihoods and countries' economies that are likely to persist for several years after the pandemic has ended. As part of the global response to COVID-19, the digital technology industry especially Fintech (Financial Technology) has been instrumental in creating and delivering services and technologies that have mitigated, at least in part, the pandemic's effect on multiple aspects of people's lives in every part of the world. The Fintech companies and Start-ups have played a tremendous role in mitigating the inconvenience for the common people by implementing the next generation solutions for everyone and the people were quick to adopt to such a change. The interest and surge of usage of Fintech applications (Digital applications related to Finance/Payments/Banking/Ecommerce) carries in preserving people’s livelihoods and businesses that have been, and continue to be, threatened by the pandemic COVID-19. This working paper will discuss about the impact of digital payments in the developing country like India during COVID-19, Pre-COVID-19, evolution of digital transformation in India from the past few years, People adoption to digital technologies, digital literacy intensification in emerging markets and also discusses the future trends of Fintech and its impact on consumers and businesses even after the pandemic ends.
Twitter based Data Analysis in Natural Language Processing using a Novel Catboost Recurrent Neural Framework

In recent years, the sentiment analysis using Twitter data is the most prevalent theme in Natural Language Processing (NLP).However, the existing sentiment analysis approaches are having lower performance and accuracy for classification due to the inadequate labeled data and failure to analyze the complex sentences.So, this research develops the novel hybrid machine learning model as Catboost Recurrent Neural Framework (CRNF) with an error pruning mechanism to analyze the Twitter data based on user opinion.Initially, the twitter-based dataset is collected that tweets based on the coronavirus COVID-19 vaccine, which are pre-processed and trained to the system.Furthermore, the proposed CRNF model classifies the sentiments as positive, negative, or neutral.Moreover, the process of sentiment analysis is done through Python and the parameters are calculated.Finally, the attained results in the performance parameters like precision, recall, accuracy and error rate are validated with existing methods.
A finite-state morphological analyser for Paraguayan Guaraní

This article describes the development of morphological analyser for Paraguayan Guaraní, an agglutinative indigenous language spoken by nearly 6 million people in South America.The implementation of our analyser uses HFST (Helsiki Finite State Technology) to model morphotactics and phonological processes occurring in Guaraní.We assess the efficacy of the approach on publically available corpora and find that the naïve coverage of analyser is between 86% and 91%.
Django: Web Development Simple & Fast

This Research paper studies about a python-based web framework known as Django. It is an open source framework which follows the basic Model View Template structure with some modifications which are explained inside the paper and it also help us to know why we use Django over other web frameworks that are available in the industry and how we can install it on our system and create a basic project using this framework following this our paper also studies about the different modules that are available to us this also helps us understand the MVT structure of this framework very briefly. When we go deep inside the paper, we also get to know how we can create apps inside the framework and add them to our main project and how we can create form inside views how it interacts with databases and how it performs operations on databases how it makes easy making dynamic websites and making task easy by centralizing access to the apps. Basically, this paper covers everything required for a person or a student to get started with the django framework and learn the basics to create some simple projects related to the Django web framework and make learning interactive and easy even for a lame person.
Alfabetización del profesorado en gamificación mediada por las TIC. Estado del arte

Recognizing the need for teacher literacy, highly supported by educational research, this work aims to carry out a reflective exercise on the educational potential of gamification mediated by Information and Communication Technologies (ICT) and its inclusion in the educational curriculum, offering all those aspects that a teacher who wishes to start in the gamification of their subject should know. To do this, following the methodological parameters of the state of the art, the concepts related to the term, such as learning through games or gamification, among others, are reviewed, as well as the features and characteristics of this active methodology and the educational impact it has in students, considering the perspective of different authors and the results of various investigations. Finally, some ICT tools and psychopedagogical guidelines are exposed that facilitate the implementation of gamification in the classroom by teachers. Every literacy process implies reaching the teaching competence level that contributes to the experiential transfer of knowledge to the classroom. With this intention this article has been constructed, wishing that it was inspiring and that it promoted the teaching methodological change.
What Social Media Facilitates, Social Media should Regulate: Duties in the New Public Sphere

Abstract This article offers a distinctive way of grounding the regulative duties held by social media companies (SMCs). One function of the democratic state is to provide what we term the right to democratic epistemic participation within the public sphere. But social media has transformed our public sphere, such that SMCs now facilitate citizens’ right to democratic epistemic participation and do so on a scale that was previously impossible. We argue that this role of SMCs in expanding the scope of what counts as fair democratic epistemic participation, and in becoming the providers of access to the digital public sphere, brings with it duties of regulation.
Multi-Grained Knowledge Distillation for Named Entity Recognition

Although pre-trained big models (e.g., BERT, ERNIE, XLNet, GPT3 etc.) have delivered top performance in Seq2seq modeling, their deployments in real-world applications are often hindered by the excessive computations and memory demand involved.For many applications, including named entity recognition (NER), matching the state-of-the-art result under budget has attracted considerable attention.Drawing power from the recent advance in knowledge distillation (KD), this work presents a novel distillation scheme to efficiently transfer the knowledge learned from big models to their more affordable counterpart.Our solution highlights the construction of surrogate labels through the k-best Viterbi algorithm to distill knowledge from the teacher model.To maximally assimilate knowledge into the student model, we propose a multigrained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning.To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts.We further discuss ablation results to dissect our gains.
The Specifics of Training Highly Qualified Personnel in Postgraduate Studies Using Information and Communication Technologies in Russia and Abroad

Modern requirements for Russian higher education in general and the system of training highly qualified personnel in particular arise from the necessity to improve the quality and efficiency in order to ensure the countrys international competitiveness in important areas of life. The research is aimed at filling the need for a detailed analysis of algorithms, frameworks and principles of organizing the process of training highly qualified personnel in graduate school, using information and communication technologies (ICT). The comparative nature of the work, which analyzes Russian and international experiences in this field, allows to obtain data that have not previously been systematized in the present context. The study is based on scientific and pedagogical publications of Russian and international authors. The purpose of the study is to consider the theoretical, methodological and organizational aspects of training graduate students, using ICTs in the selection of the analyzed material. The comparative analysis makes it possible to identify and systematize the specifics of informatization in training scientific and pedagogical personnel, the current implicit state of which complicates its interpretation in terms of the educational process organization and management. As a result, we can identify the following eight characteristics of the system of training highly qualified personnel in postgraduate studies using ICTs: theoretical and methodological foundations, characteristics of electronic learning systems, conditions for effective application and input data for their design, requirements for scientific supervisors and students, factors that update this type of training and a list of basic terms and concepts. The described characteristics can be used as a theoretical basis for further research, for example, in order to determine the actual parameters of designing the environment for students in graduate school as a means of scientific and pedagogical training of highly qualified personnel. The given descriptions of technologies in the context of the peculiarities of the impact of informatization on the implementation of the educational process, together with references to sources, allow to use the materials of the article as data for its planning and organization. The results of the research can be useful for specialists in designing, organizing, implementing and supporting the educational process in higher education using ICT tools: research supervisors, graduate students, managers and designers of educational programs.
KnowCrawler: AI Classification Cloud-Driven Framework for Web Crawling Using Collective Knowledge

The need for Web Crawler in search engine optimization is very important, As the World Wide Web is huge and contains large amount of URL’s it is important to rank these URL’s and display more relevant web pages when a topic is searched in the search engine. AI cloud is a classification system which has a scheduler, dispatcher and a classification unit which uses bagging classifier and does parallel processing of data. The implication of the proposed approach is to build a Knowledge driven paradigm for crawling of web pages from the Web. Also, the approach focuses on integrating AI Classification Cloud and reduce the processing time for classification. The existing systems is not fully Semantic and is not compatible with standards of the Semantic Web. This paper solves the problem of lack of diversity and clod start problem in web crawling. This paper proposes an AI classification cloud driven framework to this problem with a use of Collective knowledge and ontology, and Cuckoo search. The architectures performance is evaluated and compared with the baseline approaches and it is seen that the proposed solution is higher in terms of performance and recorded a Harvest Rate of 94.41.
A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

Objectives: Current research aims to address the challenges of exchanging healthcare information, since when this information has to be shared, this happens by specifically designed medical applications or even by the patients themselves. Among the problems that the Health Information Exchange (HIE) initiative is facing are that (i) third party health data cannot be accessed without internet, (ii) there exist crucial delays in accessing citizens’ data, (iii) the direct HIE can only happen among Healthcare Institutions. Methods: Towards the solution of these issues, a Device-to-Device (D2D) protocol has been specified, running on top of the Bluetooth protocol for efficient data exchange. This research is focused on this D2D protocol, by comparing the different Bluetooth profiles that can be used for transmitting this data, based on specific metrics considering the probabilities of transferring erroneous data. Findings: An evaluation of three Bluetooth profiles takes place, concluding that two of the three profiles must be used to respect the D2D protocol nature and be fully supported by the main market vendors’ operating systems. Novelty:Based on this evaluation, the specified D2D protocol has been built on top of state-of-the-art short-range distance communication technologies, fully supporting the healthcare ecosystem towards the HIE paradigm. Doi: 10.28991/esj-2021-01276 Full Text: PDF
Learning to Ask Conversational Questions by Optimizing Levenshtein Distance

Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis.Existing maximum likelihood estimation based methods often get trapped in easily learned tokens as all tokens are treated equally during training.In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance through explicit editing actions.RISE is able to pay attention to tokens that are related to conversational characteristics.To train RISE, we devise an Iterative Reinforce Training (IRT) algorithm with a Dynamic Programming based Sampling (DPS) process to improve exploration.Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods and generalizes well on unseen data.
Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving

With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems.However, these SOTA solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas.As a result, the expression trees they produce are lengthy and uninterpretable because they need to use multiple operators and constants to represent one single formula.In this paper, we propose sequence-to-general tree (S2G) that learns to generate interpretable and executable operation trees where the nodes can be formulas with an arbitrary number of arguments.With nodes now allowed to be formulas, S2G can learn to incorporate mathematical domain knowledge into problem-solving, making the results more interpretable.Experiments show that S2G can achieve a better performance against strong baselines on problems that require domain knowledge. 1
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network

We present an instance-based nearest neighbor approach to entity linking.In contrast to most prior entity retrieval systems which represent each entity with a single vector, we build a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions of different entities.This approach allows all mentions of an entity to serve as "class prototypes" as inference involves retrieving from the full set of labeled entity mentions in the training set and applying the nearest mention neighbor's entity label.Our model is trained on a large multilingual corpus of mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor inference on an index of 700 million mentions.It is simpler to train, gives more interpretable predictions, and outperforms all other systems on two multilingual entity linking benchmarks.