Reviewerly/exHarmony huggingface dataset:
Natural Language Processing for the Analysis Sentiment using a LSTM Model

Over the past decade, social networks have revolutionised the communication between organisations and their customers, and the data provided by customers on social network platforms is having an increasingly important impact on how organisations collect and analyse this data to make better decisions.We have prepared a new dataset that will allow the scientific community to estimate and evaluate new models using nearly the same conditions.Moreover, this dataset represents a recent and interesting sample for the proposed machine learning models to correctly identify the topics or points on which the company should focus to improve customer satisfaction and better meet their needs.Therefore, we have proposed a recurrent neural network (RNN) with Long short-term memory (LSTM) that we will run in the cloud to predict sentiment analysis.The objective is also to define systems capable of extracting subjective information from natural language texts, such as feelings and opinions, with the aim of creating structured knowledge that can be used by a decision support system or a decision maker for better customer management.The proposed neural network has been trained on the proposed dataset which contains 50 000 customer observations.The performance of the proposed architecture is very important as the success rate is 96%.
Software Project Duration Estimation Based on COSMIC Method Applied to Data Flow Diagram

Business process models are created before the detailed software design, during requirements phase of software development. Even disciplined agile methodology includes business process modeling, before development iterations start. Their use in an estimation of software development duration could be beneficial, because they provide sufficient elements for mapping with software design and development planning. In this paper we propose the method for software project duration estimation based on Common Software Measurement International Consortium (COSMIC) method, applied to data flow diagram. This method is based on data flow diagram analysis and extraction of primitive business procceses,data flows and data stores. The paper contributes with the approach to enhance COSMIC method with calculation of software development process duration based on both data movement-related and data-manipulation-related software functional sub-processes. Data movement-related functional sub-processes are derived from Create, Read, Update, Delete (CRUD) operations assigned to data flows. Calculation of data manipulation-related functional sub-processes duration is derived from number of business processes. The proposed metric enables calculating effort (expressed in Cosmic Functional Points units) and duration (presented with Man/Hours units). An example of the approach application and an empirical study demonstrates the applicability of the approach.
IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection

Our system, IUCL, participated in the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification.Our main goal in building this system is to investigate how the use of demographic attributes influences performance.Our results show that our text-only systems perform very competitively, ranking first in the empathy detection task, reaching an average Pearson correlation of 0.54, and second in the emotion classification task, reaching a Macro-F of 0.572.Our systems that use both text and demographic data are less competitive.
Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models

In many natural language processing (NLP) tasks the same input (e.g.source sentence) can have multiple possible outputs (e.g.translations).To analyze how this ambiguity (also known as intrinsic uncertainty) shapes the distribution learned by neural sequence models we measure sentence-level uncertainty by computing the degree of overlap between references in multi-reference test sets from two different NLP tasks: machine translation (MT) and grammatical error correction (GEC).At both the sentence-and the task-level, intrinsic uncertainty has major implications for various aspects of search such as the inductive biases in beam search and the complexity of exact search.In particular, we show that well-known pathologies such as a high number of beam search errors, the inadequacy of the mode, and the drop in system performance with large beam sizes apply to tasks with high level of ambiguity such as MT but not to less uncertain tasks such as GEC.Furthermore, we propose a novel exact n-best search algorithm for neural sequence models, and show that intrinsic uncertainty affects model uncertainty as the model tends to overly spread out the probability mass for uncertain tasks and sentences.
EntangleNetSat: A Satellite-Based Entanglement Resupply Network

In the practical context of quantum networks, quantum teleportation plays a key role in transmitting quantum information. In the process of teleportation, a maximally entangled pair is consumed. Through this paper, an efficient scheme of re-establishing entanglement between different nodes in a quantum network is explored. A hybrid land-satellite network is considered, where the land-based links are used for short-range communication, and the satellite links are used for transmissions between distant nodes. This new scheme explores many different possibilities of resupplying the land nodes with entangled pairs, depending on: the position of the satellites, the number of pairs available and the distance between the nodes themselves. As to make the entire process as efficient as possible, we consider the situations of direct transmissions of entangled photons and also the transmissions making use of entanglement swapping. An analysis is presented for concrete scenarios, sustained by numerical data.
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning

Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be surprisingly effective for cross-lingual transfer of syntactic parsing models (Wu and Dredze, 2019), but only between related languages.However, source and training languages are rarely related, when parsing truly low-resource languages.To close this gap, we adopt a method from multi-task learning, which relies on automated curriculum learning, to dynamically optimize for parsing performance on outlier languages.We show that this approach is significantly better than uniform and size-proportional sampling in the zero-shot setting.
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning

Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality.Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the objective function(s) that are used for learning and inference.Recent unsupervised sentence compression approaches use custom objectives to guide discrete search; however, guided search is expensive at inference time.In this work, we explore the use of reinforcement learning to train effective sentence compression models that are also fast when generating predictions.In particular, we cast the task as binary sequence labelling and fine-tune a pre-trained transformer using a simple policy gradient approach.Our approach outperforms other unsupervised models while also being more efficient at inference time.
Towards Explainability of Tree-Based Ensemble Models. A Critical Overview

Tree-based ensemble models are widely applied in artificial intelligence systems due to their robustness and generality. However, those models are not transparent. For the sake of making systems trustworthy and dependable, multiple explanation techniques are developed. This paper presents selected explainability techniques for tree-based ensemble models. First, the aspect of black-boxness and the definition of explainability are reported. Then, predominant model-agnostic (LIME, SHAP, counterfactual explanations), as well as model-specific techniques (fusion into a single decision tree, iForest) are described. Moreover, other methods are also briefly mentioned. Finally, a brief summary is presented.
SSNCSE_NLP@LT-EDI-ACL2022:Hope Speech Detection for Equality, Diversity and Inclusion using sentence transformers

In recent times, applications have been developed to regulate and control the spread of negativity and toxicity on online platforms.The world is filled with serious problems like political & religious conflicts, wars, pandemics, and offensive hate speech is the last thing we desire.Our task was to classify a text into 'Hope Speech' and 'Non-Hope Speech'.We searched for datasets acquired from YouTube comments that offer support, reassurance, inspiration, and insight, and the ones that don't.The datasets were provided to us by the LTEDI organizers in English, Tamil, Spanish, Kannada, and Malayalam.To successfully identify and classify them, we employed several machine learning transformer models such as m-BERT, MLNet, BERT, XLMRoberta, and XLM_MLM.The observed results indicate that the BERT and m-BERT have obtained the best results among all the other techniques, gaining a weighted F1score of 0.92, 0.71, 0.76, 0.87, and 0.83 for English, Tamil, Spanish, Kannada, and Malayalam respectively.This paper depicts our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LTEDI 2021.
On Masked Language Models for Contextual Link Prediction

In the real world, many relational facts require context; for instance, a politician holds a given elected position only for a particular timespan.This context (the timespan) is typically ignored in knowledge graph link prediction tasks, or is leveraged by models designed specifically to make use of it (i.e.n-ary link prediction models).Here, we show that the task of n-ary link prediction is easily performed using language models, applied with a basic method for constructing cloze-style query sentences.We introduce a pre-training methodology based around an auxiliary entity-linked corpus that outperforms other popular pre-trained models like BERT, even with a smaller model.This methodology also enables n-ary link prediction without access to any n-ary training set, which can be invaluable in circumstances where expensive and time-consuming curation of n-ary knowledge graphs is not feasible.We achieve state-ofthe-art performance on the primary n-ary link prediction dataset WD50K and on WikiPeople facts that include literals -typically ignored by knowledge graph embedding methods.
Findings of the Shared Task on Offensive Span Identification fromCode-Mixed Tamil-English Comments

Offensive content moderation is vital in social media platforms to support healthy online discussions.However, their prevalence in codemixed Dravidian languages is limited to classifying whole comments without identifying part of it contributing to offensiveness.Such limitation is primarily due to the lack of annotated data for offensive spans.Accordingly, in this shared task, we provide Tamil-English code-mixed social comments with offensive spans.This paper outlines the dataset so released, methods, and results of the submitted systems.
APPLICATION OF VIRTUAL REALITY TECHNOLOGIES IN THE TRAINING PROCESS

The article is devoted to discussing the possibilities of using virtual reality technologies in organizing the training process and conducting training sessions. The authors consider training with the use of virtual reality technologies as one of the most effective means for maintaining physical performance and technical and tactical readiness during remote training sessions. This paper presents both the advantages, which lie in the fact that a number of restrictions imposed by real sports conditions are removed, and the disadvantages of using virtual reality technologies in sports, including elite sports, to which the authors include problems associated with technologies and equipment. The article also discusses a number of requirements for virtual environments and equipment, the observance of which ensures the creation of a realistic immersive environment. The authors concluded that the training process using virtual reality technologies is an effective way to train an athlete along with other methods.
ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine Learning

We present the Interactive Classification System (ICS), a web-based application that supports the activity of manual text classification.The application uses machine learning to continuously fit automatic classification models that are in turn used to actively support its users with classification suggestions.The key requirement we have established for the development of ICS is to give its users total freedom of action: they can at any time modify any classification schema and any label assignment, possibly reusing any relevant information from previous activities.We investigate how this requirement challenges the typical scenarios faced in machine learning research, which instead give no active role to humans or place them into very constrained roles, e.g., on-demand labeling in active learning processes, and always assume some degree of batch processing of data.We satisfy the "total freedom" requirement by designing an unobtrusive machine learning model, i.e., the machine learning component of ICS as an unobtrusive observer of the users, that never interrupts them, continuously adapts and updates its models in response to their actions, and it is always available to perform automatic classifications.Our efficient implementation of the unobtrusive machine learning model combines various machine learning methods and technologies, such as hash-based feature mapping, random indexing, online learning, active learning, and asynchronous processing.
D2BGAN: A Dark to Bright Image Conversion Model for Quality Enhancement and Analysis Tasks Without Paired Supervision

This paper presents an image enhancement model, D2BGAN (Dark to Bright Generative Adversarial Network), to translate low light images to bright images without a paired supervision.We introduce the use of geometric and lighting consistency along with a contextual loss criterion.These when combined with multiscale color, texture and edge discriminators prove to provide competitive results.We performed extensive experiments using benchmark datasets to visually and objectively compare our results.We observed the performance of D2BGAN on real-time driving datasets that are subject to motion blur, noise, and other artifacts.We further demonstrated that our enhanced images can be profitably used in image-understanding tasks.Images processed using our technique obtain the best or second best average scores for three different image quality evaluation methods on the Naturalness Preserved Enhancement (NPE), Low Light Image Enhancement (LIME), Multi-Exposure Image Fusion (MEF) benchmark datasets.Best scores are also obtained on the LOw-Light (LOL) test set and on Berkeley Driving Dataset (BDD) images processed with D2BGAN.Face detection tasks on the DarkFace benchmark dataset show an mAP (mean Average Precision) improvement from 0.209 to 0.301 when images are processed using D2BGAN.mAP further improves to 0.525 when finetuning techniques are adopted.
Overview of 3D Human Pose Estimation

3D human pose estimation is a major focus area in the field of computer vision, which plays an important role in practical applications.This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos.An overall perspective of methods integrated with deep learning is introduced.Novel image-based and video-based inputs are proposed as the analysis framework.From this viewpoint, common problems are discussed.The diversity of human postures usually leads to problems such as occlusion and ambiguity, and the lack of training datasets often results in poor generalization ability of the model.Regression methods are crucial for solving such problems.Considering image-based input, the multi-view method is commonly used to solve occlusion problems.Here, the multi-view method is analyzed comprehensively.By referring to video-based input, the human prior knowledge of restricted motion is used to predict human postures.In addition, structural constraints are widely used as prior knowledge.Furthermore, weakly supervised learning methods are studied and discussed for these two types of inputs to improve the model generalization ability.The problem of insufficient training datasets must also be considered, especially because 3D datasets are usually biased and limited.Finally, emerging and popular datasets and evaluation indicators are discussed.The characteristics of the datasets and the relationships of the indicators are explained and highlighted.Thus, this article can be useful and instructive for researchers who are lacking in experience and find this field confusing.In addition, by providing an overview of 3D human pose estimation, this article sorts and refines recent studies on 3D human pose estimation.It describes kernel problems and common useful methods, and discusses the scope for further research.
Incremental Learning based Optimized Sentiment Classification using Hybrid Two-Stage LSTM-SVM Classifier

Sentiment analysis is a subtopic of Natural Language Processing (NLP) techniques that involves extracting emotions from unprocessed text.This is commonly used on customer review posts to automatically determine if user / customer sentiments are negative or positive.But quality of these analysis is completely dependent on its quantity of raw data.The conventional classifier-based sentiment prediction is not capable to handle these large datasets.Hence, for an efficient and effective sentiment prediction, deep learning approach is used.The proposed system consists of three main phases, such as 1) Data collection and pre-processing, 2) Count vectorizer and dimensionality reduction is used for feature extraction, 3) Hybrid classifier LSTM-SVM using incremental learning.Initially the input raw data is gathered from the e-commerce sites for product reviews and collected raw is given to pre-processing, which do tokenization, stop word removal, lemmatization for each review text.After pre-processing, features like keywords, length, and word count are extracted and given to feature extraction stage.Then a hybrid classifier using two-stage LSTM and SVM is developed for training the sentimental classes by passing new features and classes for incremental learning.The proposed system is developed using python and it is compared with the state-of-the-art classification techniques.The performance of the proposed system is compared based on performance metrics such as accuracy, precision, recall, sensitivity, specificity etc.The proposed model performed an accuracy of 92% which is better compared to the state-of-the-art existing techniques.
Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil

This paper illustrates the overview of the shared task on automatic speech recognition in the Tamil language.In the shared task, spontaneous Tamil speech data gathered from elderly and transgender people was given for recognition and evaluation.These utterances were collected from people when they communicated in the public locations such as hospitals, markets, vegetable shop, etc.The speech corpus includes utterances of male, female, and transgender and was split into training and testing data.The given task was evaluated using WER (Word Error Rate).The participants used the transformer-based model for automatic speech recognition.Different results using different pre-trained transformer models are discussed in this overview paper.
CUET-NLP@TamilNLP-ACL2022: Multi-Class Textual Emotion Detection from Social Media using Transformer

Recently, emotion analysis has gained increased attention by NLP researchers due to its various applications in opinion mining, e-commerce, comprehensive search, healthcare, personalized recommendations and online education.Developing an intelligent emotion analysis model is challenging in resourceconstrained languages like Tamil.Therefore a shared task is organized to identify the underlying emotion of a given comment expressed in the Tamil language.The paper presents our approach to classifying the textual emotion in Tamil into 11 classes: ambiguous, anger, anticipation, disgust, fear, joy, love, neutral, sadness, surprise and trust.We investigated various machine learning (LR, DT, MNB, SVM), deep learning (CNN, LSTM, BiLSTM) and transformer-based models (Multilingual-BERT, XLM-R).Results reveal that the XLM-R model outdoes all other models by acquiring the highest macro f 1 -score (0.33).
Revisiting Orthogonality Regularization: A Study for Convolutional Neural Networks in Image Classification

Recent research in deep Convolutional Neural Networks (CNN) faces the challenges of vanishing/exploding gradient issues, training instability, and feature redundancy.Orthogonality Regularization (OR), which introduces a penalty function considering the orthogonality of neural networks, could be a remedy to these challenges but is surprisingly not popular in the literature.This work revisits the OR approaches and empirically answer the question: Even when comparing various regularizations like weight decay and spectral norm regularization, which is the most powerful OR technique?We begin by introducing the improvements of various regularization techniques, specifically focusing on OR approaches over a variety of architectures.After that, we disentangle the benefits of OR in the comparison of other regularization approaches with a connection on how they affect norm preservation effects and feature redundancy in the forward and backward propagation.Our investigations show that Kernel Orthogonality Regularization (KOR) approaches, which directly penalize the orthogonality of convolutional kernel matrices, consistently outperform other techniques.We propose a simple KOR method considering both row-and column-orthogonality, of which empirical performance is the most effective in mitigating the aforementioned challenges.We further discuss several circumstances in the recent CNN models on various benchmark datasets, wherein KOR gains more effectiveness.
From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains

We present a generalized paradigm for adaptation of propositional analysis (predicateargument pairs) to new tasks and domains.We leverage an analogy between stances (beliefdriven sentiment) and concerns (topical issues with moral dimensions/endorsements) to produce an explanatory representation.A key contribution is the combination of semi-automatic resource building for extraction of domaindependent concern types (with 2-4 hours of human labor per domain) and an entirely automatic procedure for extraction of domainindependent moral dimensions and endorsement values.Prudent (automatic) selection of terms from propositional structures for lexical expansion (via semantic similarity) produces new moral dimension lexicons at three levels of granularity beyond a strong baseline lexicon.We develop a ground truth (GT) based on expert annotators and compare our concern detection output to GT, to yield 231% improvement in recall over baseline, with only a 10% loss in precision.F1 yields 66% improvement over baseline and 97.8% of human performance.Our lexically based approach yields large savings over approaches that employ costly human labor and model building.We provide to the community a newly expanded moral dimension/value lexicon, annotation guidelines, and GT.