Reviewerly/exHarmony huggingface dataset:
Text Summarization on Telugu e-News based on Long-Short Term Memory with Rectified Adam Optimizer

Text summarization is a natural language processing method that reduces the text in the article and provides the important information from the document.Few researches have been carried out in the text summarization in the Telugu language and have lower efficiency in Telugu Text summarization.In this research, the Long-Short Term Memory (LSTM) with Rectified Adam Optimizer (RAdam) method and focal loss function is proposed for text summarization in Telugu e-news data.The Eenadu Telugu enews data of various categories are collected to evaluate the performance of the proposed LSTM with RAdam method.Tokenization method is applied in the pre-processing method to extract the important keywords from the input data.Focal loss function is applied between the cells of LSTM to handle the imbalance data problem.Modulation function in the focal loss function down weight the easy examples to focus on hard examples and effectively handles the imbalance data.The proposed LSTM with RAdam method has advantage of using Exponential Moving Average (EMA) for adaptive learning rate and rectify the variance.The proposed LSTM with RAdam method is evaluated in 10 categories of e-news data to analysis the performance.This proposed LSTM with RAdam method has 93.46% accuracy and existing LSTM method has 86.92% accuracy.
Development of the new pedagogical skills in the context of European integration

The processes of globalization and informatization have a great impact on Ukrainian higher education system. In this regard, the problems of European integration of national higher education in the field of international standards occur. This article is aimed at developing a conceptual model of scientific and educational process and pedagogical skills of higher education teachers based on European integration. The necessity of using the innovative approach and the need to improve the capacity of universities to implement research and development technologies at the national level were emphasized. Innovative model of development of pedagogical skills of higher education teachers based on European integration was formed. Criteria for assessing the level of motivational and value component of pedagogical skills of the teacher were identified. The advantages and disadvantages of the development strategy of Ukrainian universities in the context of the needs of European integration of scientific and educational space were defined. It was concluded that the participation of teachers in promising research and competitions will ensure a steady increase in public funding for research in universities. The national higher education of Ukraine should be an open dynamic system.
Guideline Bias in Wizard-of-Oz Dialogues

NLP models struggle with generalization due to sampling and annotator bias.This paper focuses on a different kind of bias that has received very little attention: guideline bias, i.e., the bias introduced by how our annotator guidelines are formulated.We examine two recently introduced dialogue datasets, CCPE-M and Taskmaster-1, both collected by trained assistants in a Wizard-of-Oz set-up.For CCPE-M, we show how a simple lexical bias for the word like in the guidelines biases the data collection.This bias, in effect, leads to poor performance on data without this bias: a preference elicitation architecture based on BERT suffers a 5.3% absolute drop in performance, when like is replaced with a synonymous phrase, and a 13.2% drop in performance when evaluated on out-of-sample data.For Taskmaster-1, we show how the order in which instructions are presented, biases the data collection.
Intellectual Property in the Context of Global Ethics

Today we are the subjects and witnesses of the continuous development and transformation of society, which has made rapid leaps from modern to the informational and knowledge-based society. In this sense, in order to ensure technological, economic and social progress, Intellectual Property Rights (IPR) must become the only key element for protecting ideas, stimulating innovation and creativity, designing and contributing to the creation of technology. In this article the authors emphasize the need to address IPR in the context of global ethics, because IPR is also the social responsibility and moral duty of every global citizen, whether creative or consumer, to respect the cultural and intellectual heritage of humanity and to contribute to its diversification and to overcome the challenges of the contemporary world in the digital age. In the current context of the globalization of economic, social and even spiritual life, the need for morality of people is becoming more and more pressing, on which we will continue to focus our concerns, because any new issue addressed includes a component of global ethics.
Neural Metaphor Detection with Visibility Embeddings

We present new results for the problem of sequence metaphor labeling, using the recently developed Visibility Embeddings.We show that concatenating such embeddings to the input of a BiLSTM obtains consistent and significant improvements at almost no cost, and we present further improved results when visibility embeddings are combined with BERT.
Levi Graph AMR Parser using Heterogeneous Attention

Coupled with biaffine decoders, transformers have been effectively adapted to text-to-graph transduction and achieved state-of-the-art performance on AMR parsing.Many prior works, however, rely on the biaffine decoder for either or both arc and label predictions although most features used by the decoder may be learned by the transformer already.This paper presents a novel approach to AMR parsing by combining heterogeneous data (tokens, concepts, labels) as one input to a transformer to learn attention, and use only attention matrices from the transformer to predict all elements in AMR graphs (concepts, arcs, labels).Although our models 1 use significantly fewer parameters than the previous state-of-the-art graph parser, they show similar or better accuracy on AMR 2.0 and 3.0.
Blockchain based Video Conferencing System with Enhanced Data Integrity Protection Auditability

With the belief of every person should have access to a platform to share their voice, video conferencing is increasingly becoming the most genuine way to communicate with the trusted parties.But, due to the potential vulnerabilities of communication in-between entities exchanging information in a video conferencing systems may lead to catastrophes.In this research, a decentralized video conferencing systems is proposed to establish trusted communication in-between entities of the system and to enhance the integrity protection of stored information by utilizing the blockchain technology.As it is based on decentralized transaction ledger of blockchain, auditability, reliability of the exchanging information and the entities are enhanced.Each and every transaction of the system is traced and by performing intrusion detection, system is protected from malicious impersonators and intrusions.
Extracting Events from Industrial Incident Reports

Incidents in industries have huge social and political impact and minimizing the consequent damage has been a high priority.However, automated analysis of repositories of incident reports has remained a challenge.In this paper, we focus on automatically extracting events from incident reports.Due to absence of event annotated datasets for industrial incidents we employ a transfer learning based approach which is shown to outperform several baselines.We further provide detailed analysis regarding effect of increase in pre-training data and provide explainability of why pre-training improves the performance.
Data augmentation for low-resource grapheme-to-phoneme mapping

In this paper we explore a very simple neural approach to mapping orthography to phonetic transcription in a low-resource context.The basic idea is to start from a baseline system and focus all efforts on data augmentation.We will see that some techniques work, but others do not.
Facilitating Meta-Theory Reasoning (Invited Paper)

Structural proof theory is praised for being a symbolic approach to reasoning and proofs, in which one can define schemas for reasoning steps and manipulate proofs as a mathematical structure.For this to be possible, proof systems must be designed as a set of rules such that proofs using those rules are correct by construction.Therefore, one must consider all ways these rules can interact and prove that they satisfy certain properties which makes them "well-behaved".This is called the meta-theory of a proof system.Meta-theory proofs typically involve many cases on structures with lots of symbols.The majority of cases are usually quite similar, and when a proof fails, it might be because of a sub-case on a very specific configuration of rules.Developing these proofs by hand is tedious and error-prone, and their combinatorial nature suggests they could be automated.There are various approaches on how to automate, either partially or completely, meta-theory proofs.In this paper, I will present some techniques that I have been involved in for facilitating meta-theory reasoning.
Optimized Features Extraction from Spectral and Temporal Features for Identifying the Telugu Dialects by Using GMM and HMM

Telugu language is one of the historical languages and belongs to the Dravidian family. It contains three dialects named Telangana, Costa Andhra, and Rayalaseema. This paper identifies the dialects of the Telugu language. MFCC, Delta MFCC, and Delta-Delta MFCC are applied with 39 feature vectors for the dialect identification. In addition, ZCR is also applied to identify the dialects. At last combined all the MFCC and ZCR features. A standard database is created to identify the dialects of the Telugu language. Different statistical methods like HMM and GMM are applied for the classification purpose. To improve the accuracy of the model, dimensionality reduction technique PCA is applied to reduce the number of features extracted from the speech signal and applied to models. In this work, with the application of dimensionality reduction, there is an increase in the accuracy of models observed.
JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction

In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity.In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context.We participated in the sub-task of predicting the complexity score of single words.
CLaC-BP at SemEval-2021 Task 8: SciBERT Plus Rules for MeasEval

This paper explains the design of a heterogeneous system that ranked eighth in competition in SemEval2021 Task 8. We analyze ablation experiments and demonstrate how the system components, namely tokenizer, unit identifier, modifier classifier, and language model, affect the overall score.We compare our results to similar experiments from the literature and introduce a grouping algorithm developed in the post-evaluation phase that increased our system's overall score, hypothetically elevating our competition rank from eight to six.
Symbolic Dynamic Programming for Continuous State MDPs with Linear Program Transitions

Recent advances in symbolic dynamic programming (SDP) have significantly broadened the class of MDPs for which exact closed-form value functions can be derived. However, no existing solution methods can solve complex discrete and continuous state MDPs where a linear program determines state transitions --- transitions that are often required in problems with underlying constrained flow dynamics arising in problems ranging from traffic signal control to telecommunications bandwidth planning. In this paper, we present a novel SDP solution method for MDPs with LP transitions and continuous piecewise linear dynamics by introducing a novel, fully symbolic argmax operator. On three diverse domains, we show the first automated exact closed-form SDP solution to these challenging problems and the significant advantages of our SDP approach over discretized approximations.
Formation of group creative thinking when working with virtual walls

The problem and the aim of the study. Preparing a graduate capable of individual creative thinking, ready to work in a team, and to manage changes, projects, teams is one of the most important tasks that digital school teachers face. The effectiveness of its solution depends on the experience of collaboration, tools of virtual interaction, the formation of group thinking. The authors propose to use collective virtual walls to form group creative thinking as an important skill corresponding to the conditions of uncertainty of the future. Research methods. To activate cognition, create favorable conditions for creativity and discoveries when working together, methods of group interactive learning are used (demonstration in a remote mode of interaction, online discussion, project presentations, work on a collective virtual wall). The study was carried out on the basis of gymnasium No. 2 in Kirovo-Chepetsk. As a software tool to support online collaboration and co-creative discovery, the Trello service (https://trello.com/) was used. As a method for statistical processing of the experimental data, the Pearson’s χ2 (chi-square) criterion was used. Results. In the experimental group, students used collective virtual walls to obtain theoretical information, complete practical tasks in an interactive online environment, creative search and experience of collective discovery, group reflection and presentation of team projects. The assessment of the levels of formation of group creative thinking was carried out and statistically significant differences in the qualitative changes that occurred in the pedagogical system were revealed, χ2 emp. 2 &gt; χ2crit0.05 (6.89 &gt; 5.99). In conclusion, the authors generalized the conditions under which joint activity over a collective virtual wall contributes to the formation of group creative thinking. The conditions are: awareness of the meaning and motives of joint activity; the need for self-expression and active participation in the discussion; work according to a general plan; analysis of the progress of an individual task and the entire collective project; control and self-control; direct interpersonal communication, virtual communication, etc.
ON THE POTENTIALITY OF A CLASS OF OPERATORS RELATIVE TO LOCAL BILINEAR FORMS

The inverse problem of the calculus of variations (IPCV) is solved for a second-order ordinary differential equation with the use of a local bilinear form. We apply methods of analytical dynamics, nonlinear functional analysis, and modern methods for solving the IPCV. In the paper, we obtain necessary and sufficient conditions for a given operator to be potential relative to a local bilinear form, construct the corresponding functional, i.e., found a solution to the IPCV, and define the structure of the considered equation with the potential operator. As a consequence, similar results are obtained when using a nonlocal bilinear form. Theoretical results are illustrated with some examples.
An Experience-Based Direct Generation Approach to Automatic Image Cropping

Automatic Image Cropping is a challenging task with many practical downstream applications.The task is often divided into sub-problems -generating cropping candidates, finding the visually important regions, and determining aesthetics to select the most appealing candidate.Prior approaches model one or more of these sub-problems separately, and often combine them sequentially.We propose a novel convolutional neural network (CNN) based method to crop images directly, without explicitly modeling image aesthetics, evaluating multiple crop candidates, or detecting visually salient regions.Our model is trained on a large dataset of images cropped by experienced editors and can simultaneously predict bounding boxes for multiple fixed aspect ratios.We consider the aspect ratio of the cropped image to be a critical factor that influences aesthetics.Prior approaches for automatic image cropping, did not enforce the aspect ratio of the outputs, likely due to a lack of datasets for this task.We, therefore, benchmark our method on public datasets for two related tasks -first, aesthetic image cropping without regard to aspect ratio, and second, thumbnail generation that requires fixed aspect ratio outputs, but where aesthetics are not crucial.We show that our strategy is competitive with or performs better than existing methods in both these tasks.Furthermore, our one-stage model is easier to train and significantly faster than existing two-stage or endto-end methods for inference.We present a qualitative evaluation study, and find that our model is able to generalize to diverse images from unseen datasets and often retains compositional properties of the original images after cropping.We also find that the model can generate crops with better aesthetics than the ground truth in the MIRThumb dataset for image thumbnail generation with no fine tuning.Our results demonstrate that explicitly modeling image aesthetics or visual attention regions is not necessarily required to build a competitive image cropping algorithm.
A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text

Taxonomies provide a structured representation of semantic relations between lexical terms, acting as the backbone of many applications. The research proposed herein addresses the topic of taxonomy enrichment using an ”human-in-the-loop” semi-supervised approach. I will be investigating possible ways to extend and enrich a taxonomy using corpora of unstructured text data. The objective is to develop a methodological framework potentially applicable to any domain.
Linear Models with Python

The book belongs to the Texts in Statistical Science Series, and it presents a convenient way to learn more on both statistical modeling and statistical Python language, in continuation of the auth...
APPLICABILITY OF BLOCKCHAIN TECHNOLOGY TO MANAGE FINANCIAL ISSUES IN THE SRI LANKAN CONSTRUCTION INDUSTRY

The construction industry is one of the largest industries in any country, contributing significantly to economic growth. A range of researchers explained that the construction industry suffered from numerous issues where financial issues are more critical. Late payments, cash flow issues, and lack of security of the payment are some of them. Blockchain technology shows a potential use for managing financial activities. Therefore, this research tends to investigate the applicability of blockchain technology to manage financial issues in the Sri Lankan construction industry. A comprehensive literature survey was carried out to assess the concept of blockchain technology and identified financial issues in the construction industry. A qualitative expert opinion survey was conducted with eight construction experts who had knowledge of the blockchain technology, selected through purposive sampling to collect data in a Sri Lankan context. The collected data was analysed using the content analysis method. The findings of the expert interviews indicated that faster transactions would help to reduce late payments; removing third parties from the transaction will reduce the involvement of many parties in the transaction process; and trust, security, transparency will help to increase foreign investments. Further, the lack of knowledge in the area, legal and tax issues, low investment, and social issues will be the challenges in adopting blockchain technology to the construction industry. The research finally suggests the ways to overcome such challenges in terms of using pilot projects, government rules and regulation. Thus, the research proposes that use of blockchain technology could minimise a significant number of financial issues in the construction industry.
Backpropagation Algorithm on Implementation of Signature Recognition

Many things are required by all parties, especially in the process of recognition of one's identity, ranging from health care, maintenance of bank accounts, aviation services, immigration and others.Many ways of proving one's identity and the most popular one is using a signature.The signature is used as an identification system which serves to recognize a person's identity.Recognition process is still done manually by matching the signature by the person concerned.Therefore, the very need for a system that is able to analyze and identify the characteristics of the signature, so it can be used as an alternative to simplify the process of introducing people’s signature.Artificial neural networks can be used as one of the solutions in identification of signatures.Artificial neural network is a branch of science of artificial intelligence that is capable of processing information with the performance of certain characteristics.Artificial neural networks have some method such as perceptron, Hopfield discrete, Adaline, Backpropagation, and Kohonen.In this paper, the artificial neural network with back propagation method is applied in the process of signature and patternrecognition which provided a solution that is able to analyze and recognize people's signature.Implementation of the application of neural networks in pattern recognition signature can further be applied to any computer that handles problems in the process of matching one's data.
Adaptive Sparsity through Hybrid Regularization for Effective Image Deblurring

Objectives: To develop image deblurring algorithm to effectively recover the original image from the captured blurry and noisy image.Methods: A novel image deblurring algorithm using adaptive priors is proposed.The adaptive priors vary the sparsity induced based on factors such as the type of blur affecting the image, image region, statistical parameters regarding the blur kernel and/or statistical predictions on the blur kernel.The proposed algorithm using adaptive priors improves the quality of the deblurred result, when compared with various recent image deblurring algorithms.Finding: the work contains a case study with regard to certain standard parameters.It is observed that proposed method is better in terms of frequency response, Peak signal-tonoise ratio (PSNR) and Structural Similarity Index (SSIM) values in comparison with other priors.Novelty: the proposed priors lead to the most effective results for image deblurring using the Bayesian framework.The proposed method improves the performance by 30% in PSNR and 45% in SSIM values in dB with uniform kernel size 12 * 12 and improves performance by 30 % in PSNR and 32% SSIM with a standard deviation of 3.5.The proposed method enhances the frequency response of the real-time image restoration process.Applications: Some important applications include the restoration of medical images such as MRI images, CT images where the intensity of these radiations is maintained to avoid damage to human organs.Remote sensing images captured through drones at a specified time cannot be retaken and hence restoring such images is very important.Similarly restoring images from CCTV footage, astronomical images.