Reviewerly/exHarmony huggingface dataset:
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks

While fine-tuning pre-trained models for downstream classification is the conventional paradigm in NLP, often task-specific nuances may not get captured in the resultant models.Specifically, for tasks that take two inputs and require the output to be invariant of the order of the inputs, inconsistency is often observed in the predicted labels or confidence scores.We highlight this model shortcoming and apply a consistency loss function to alleviate inconsistency in symmetric classification.Our results show an improved consistency in predictions for three paraphrase detection datasets without a significant drop in the accuracy scores.We examine the classification performance of six datasets (both symmetric and non-symmetric) to showcase the strengths and limitations of our approach.
Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion

We advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs.Starting from well-founded assumptions about word graphs, we present simple but reliable path-reranking and topic segmentation schemes.Robustness of our method is demonstrated on datasets across multiple domains, including meetings, interviews, movie scripts, and day-to-day conversations.We also identify possible avenues to augment our heuristicbased system with deep learning.We opensource our code 1 , to provide a strong, reproducible baseline for future research into unsupervised dialogue summarization.bring home the clothes that are hanging outside boris 'll tell brian to take care of that Keywords: 'care', 'clothes', 'home', 'thanks' Megan: Are we going to take a taxi to the opera?Joseph: No, I'll take my car.Megan: Great, more convenient are we going to take a taxi to the opera ?
KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base

In this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base:(1) The actions defined in grammar are not sufficient to handle uncertain reasoning common in real-world scenarios.(2) Knowledge base information is not well exploited and incorporated into semantic parsing.To mitigate the two issues, we propose a knowledge-aware fuzzy semantic parsing framework (KaFSP).It defines fuzzy comparison operations in the grammar system for uncertain reasoning based on the fuzzy set theory.In order to enhance the interaction between semantic parsing and knowledge base, we incorporate entity triples from the knowledge base into a knowledgeaware entity disambiguation module.Additionally, we propose a multi-label classification framework to not only capture correlations between entity types and relations but also detect knowledge base information relevant to the current utterance.Both enhancements are based on pre-trained language models.Experiments on a large-scale conversational question answering benchmark demonstrate that the proposed KaFSP achieves significant improvements over previous state-of-the-art models, setting new SOTA results on 8 out of 10 question types, gaining improvements of over 10% F1 or accuracy on 3 question types, and improving overall F1 from 83.01% to 85.33%.
Spatial Feature Fusion for Biomedical Image Classification based on Ensemble Deep CNN and Transfer Learning

Biomedical imaging is a rapidly evolving field that covers different types of imaging techniques which are used for diagnostic and therapeutic purposes.It plays a vital role in diagnosis and treating health conditions of human body.Classification of different imaging modalities plays a vital role in terms of providing better care and treatment options to the patients.Advancements in technology open up the new doors for medical professionals and this involves deep learning methods for automatic image classification.Convolutional neural network (CNN) is a special class of deep learning that is applied to visual imagery.In this paper, a novel spatial feature fusion based deep CNN is proposed for classification of microscopic peripheral blood cell images.In this work, multiple transfer learning features are extracted through four pre-trained CNN architectures namely VGG19, ResNet50, MobileNetV2 and DenseNet169.These features are fused into a generalized feature space that increases the classification accuracy.The dataset considered for the experiment contains 17902 microscopic images that are categorized into 8 distinct classes.The result shows that the proposed CNN model with fusion of multiple transfer learning features outperforms the individual pre-trained CNN model.The proposed model achieved 96.10% accuracy, 96.55% F1-score, 96.40% Precision and 96.70% Recall values.
Evaluation of social bot detection models

Social bots are employed to automatically perform online social network activities; thereby, they can also be utilized in spreading misinformation and malware.Therefore, many researchers have focused on the automatic detection of social bots to reduce their negative impact on society.However, it is challenging to evaluate and compare existing studies due to difficulties and limitations in sharing datasets and models.In this study, we conduct a comparative study and evaluate four different bot detection systems in various settings using 20 different public datasets.We show that high-quality datasets covering various social bots are critical for a reliable evaluation of bot detection methods.In addition, our experiments suggest that Botometer is preferable to others in order to detect social bots.
Fully-Digital Randomization Based Side-Channel Security—Toward Ultra-Low Cost-per-Security

In this paper we formulate and re-evaluate a recently proposed randomization-based side-channel protection mechanism. The strength of the construction lies with its ability to comply with standard digital design flows and that it provides a security parameter which directly links side-channel security metrics. A detailed leakage model is provided and investigated for the first time, and it is linked to electronic parameters of the randomization mechanism. We develop guidelines and optimization for concrete ASIC constructions, and sheds light on this ultra low-cost leakage-randomization mechanism. The proposed circuit is natural to be utilized without or on top of the popular masking countermeasures. It is demonstrated to be considerably more efficient in terms of attack data-complexity as compared to low-order masking (i.e., number of shares <i>d</i> = 2). In addition, seemingly it is a nice and necessary fit to increase the noise when a too low-noise environment is expected, which impedes masking&#x2019;s theoretical security. Finally, it is discussed that the proposed mechanism is natural to be embedded with masked designs for higher security-levels (<i>d</i> &#x003E; 2) while lowering significantly their asymptotically quadratic area price-tag as <i>d</i> increase. Robustness results are provided along with post &#x0026; route cost estimations for both AES encryption and a more recently proposed permutation such as ISAP. Our design efficiently provides unprecedented three orders-of-magnitude signal-to-noise reduction with a total area-overhead of 21% and 46% for AES and Ascon-&#x03C1;, respectively. These factors are more cost-efficient than low-orders masked designs and such mechanisms are sometimes necessary when the inherent noise is not sufficient. However, the joint embedding of the proposed mechanism with masked designs potentially exponentially improve the security level they provide, all whilst enabling electronic-design friendly security mechanism.
Semantic-Based Image Retrieval Using $$R^S$$-Tree and Neighbor Graph

In this paper, we present a semantic-based image retrieval method using $$R^S$$ -Tree structure and neighbor graph. The main ideas of the paper include (1) proposing a structure by combining of $$R^S$$ -Tree and neighbor clustering graph, named NBGraphRST; (2) enriching an ontology framework to describe the semantic feature of images. Firstly, a query image is extracted as a low-level visual feature vector and retrieved on NBGraphRST to get a set of content-based similar images. Secondly, the input images are classified by the k-nearest neighbor (k-NN) method to create a set of visual vocabularies. Finally, the SPARQL query is generated to retrieve the semantics of similar images based on ontology. On the basis of the proposed method, the experiment is performed on data-sets COREL, Wang, Oxford Flowers-17.
Robust Product Classification with Instance-Dependent Noise

Noisy labels in large E-commerce product data (i.e., product items are placed into incorrect categories) are a critical issue for product categorization task because they are unavoidable, nontrivial to remove and degrade prediction performance significantly.Training a product title classification model which is robust to noisy labels in the data is very important to make product classification applications more practical.In this paper, we study the impact of instancedependent noise to performance of product title classification by comparing our data denoising algorithm and different noise-resistance training algorithms which were designed to prevent a classifier model from over-fitting to noise.We develop a simple yet effective Deep Neural Network for product title classification to use as a base classifier.Along with recent methods of stimulating instance-dependent noise, we propose a novel noise stimulation algorithm based on product title similarity.Our experiments cover multiple datasets, various noise methods and different training solutions.Results uncover the limit of classification task when noise rate is not negligible and data distribution is highly skewed.
A Design Space for Writing Support Tools Using a Cognitive Process Model of Writing

Improvements in language technology have led to an increasing interest in writing support tools.In this paper we propose a design space for such tools based on a cognitive process model of writing.We conduct a systematic review of recent computer science papers that present and/or study such tools, analyzing 30 papers from the last five years using the design space.Tools are plotted according to three distinct cognitive processes-planning, translating, and reviewing-and the level of constraint each process entails.Analyzing recent work with the design space shows that highly constrained planning and reviewing are under-studied areas that recent technology improvements may now be able to serve.Finally, we propose shared evaluation methodologies and tasks that may help the field mature.
Interpersonal Communication Research in Metaverse

As an extension of the human body, media in different forms shape different characteristics of interpersonal communication.In the next generation of Internet communication forms, "Metaverse" uses VR, AR, MR, XR and other media technologies to break the boundary between virtual and reality, making interpersonal communication more complex than before.This paper explores the possible new features of interpersonal communication in the context of the "Metaverse".Since the overall "metaverse" picture has not yet been fully realized, this paper selects science fiction films that embody the concept of "metaverse" as a frontier sample for the study of future interpersonal communication patterns.Through the content analysis of 11 science fiction films, some new features of interpersonal communication are summarized.For example, the source of communication is a human individual with a virtual avatar.Virtual characters can be divided into having or not having an entity and an autonomous mind.The communication space in the Metaverse contains both real and virtual worlds.People are experiencing multi-sensory experiences communication channels and so on.
Transformer-based Models for Arabic Online Handwriting Recognition

Transformer neural networks have increasingly become the neural network design of choice, having recently been shown to outperform state-of-the-art end-to-end (E2E) recurrent neural networks (RNNs).Transformers utilize a self-attention mechanism to relate input frames and extract more expressive sequence representations.Transformers also provide parallelism computation and the ability to capture long dependencies in contexts over RNNs.This work introduces a transformer-based model for the online handwriting recognition (OnHWR) task.As the transformer follows encoder-decoder architecture, we investigated the self-attention encoder (SAE) with two different decoders: a self-attention decoder (SAD) and a connectionist temporal classification (CTC) decoder.The proposed models can recognize complete sentences without the need to integrate with external language modules.We tested our proposed models against two Arabic online handwriting datasets: Online-KHATT and CHAW.On evaluation, SAE-SAD architecture performed better than SAE-CTC architecture.The SAE-SAD model achieved a 5% character error rate (CER) and an 18%word error rate (WER) against the CHAW dataset, and a 22% CER and a 56% WER against the Online-KHATT dataset.The SAE-SAD model showed significant improvements over existing models of the Arabic OnHWR.
MUCIC@TamilNLP-ACL2022: Abusive Comment Detection in Tamil Language using 1D Conv-LSTM

Abusive language content such as hate speech, profanity, and cyberbullying etc., which is common in online platforms is creating lot of problems to the users as well as policy makers.Hence, detection of such abusive language in user-generated online content has become increasingly important over the past few years.Online platforms strive hard to moderate the abusive content to reduce societal harm, comply with laws, and create a more inclusive environment for their users.In spite of various methods to automatically detect abusive languages in online platforms, the problem still persists.To address the automatic detection of abusive languages in online platforms, this paper describes the models submitted by our team -MUCIC to the shared task on "Abusive Comment Detection in Tamil-ACL 2022".This shared task addresses the abusive comment detection in native Tamil script texts and codemixed Tamil texts.To address this challenge, two models: i) n-gram-Multilayer Perceptron (n-gram-MLP) model utilizing MLP classifier fed with char-n gram features and ii) 1D Convolutional Long Short-Term Memory (1D Conv-LSTM) model, were submitted.The n-gram-MLP model fared well among these two models with weighted F1-scores of 0.560 and 0.430 for code-mixed Tamil and native Tamil script texts, respectively.This work may be reproduced using the code available in Gthub 1 .
ConTinTin: Continual Learning from Task Instructions

The mainstream machine learning paradigms for NLP often work with two underlying presumptions.First, the target task is predefined and static; a system merely needs to learn to solve it exclusively.Second, the supervision of a task mainly comes from a set of labeled examples.A question arises: how to build a system that can keep learning new tasks from their instructions?This work defines a new learning paradigm ConTinTin (Continual Learning from Task Instructions), in which a system should learn a sequence of new tasks one by one, each task is explained by a piece of textual instruction.The system is required to (i) generate the expected outputs of a new task by learning from its instruction, (ii) transfer the knowledge acquired from upstream tasks to help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even improve the performance on earlier tasks after learning new tasks (i.e., backward-transfer).This new problem is studied on a stream of more than 60 tasks, each equipped with an instruction.Technically, our method InstructionSpeak contains two strategies that make full use of task instructions to improve forward-transfer and backward-transfer: one is to learn from negative outputs, the other is to re-visit instructions of previous tasks.To our knowledge, this is the first time to study ConTinTin in NLP.In addition to the problem formulation and our promising approach, this work also contributes to providing rich analyses for the community to better understand this novel learning problem.
One-Shot Fiat-Shamir-Based NIZK Arguments of Composite Residuosity and Logarithmic-Size Ring Signatures in the Standard Model

The standard model security of the Fiat-Shamir transform has been an active research area for many years. In breakthrough results, Canetti et al. (STOC’19) and Peikert-Shiehian (Crypto’19) showed that, under the Learning-With-Errors ( $$\mathsf {LWE}_{}$$ ) assumption, it provides soundness by applying correlation-intractable (CI) hash functions to so-called trapdoor $$\varSigma $$ -protocols. In order to be compatible with CI hash functions based on standard $$\mathsf {LWE}_{}$$ assumptions with polynomial approximation factors, all known such protocols have been obtained via parallel repetitions of a basic protocol with binary challenges. In this paper, we consider languages related to Paillier’s composite residuosity assumption ( $$\mathsf {DCR}$$ ) for which we give the first trapdoor $$\varSigma $$ -protocols providing soundness in one shot, via exponentially large challenge spaces. This improvement is analogous to the one enabled by Schnorr over the original Fiat-Shamir protocol in the random oracle model. Using the correlation-intractable hash function paradigm, we then obtain simulation-sound NIZK arguments showing that an element of $$\mathbb {Z}_{N^2}^*$$ is a composite residue, which opens the door to space-efficient applications in the standard model. As a concrete example, we build logarithmic-size ring signatures (assuming a common reference string) with the shortest signature length among schemes based on standard assumptions in the standard model. We prove security under the $$\mathsf {DCR}$$ and $$\mathsf {LWE}_{}$$ assumptions, while keeping the signature size comparable with that of random-oracle-based schemes.
Natural Language Processing for Multilingual Task-Oriented Dialogue

Recent advances in deep learning have also enabled fast progress in the research of task-oriented dialogue (ToD) systems. However, the majority of ToD systems are developed for English and merely a handful of other widely spoken languages, e.g., Chinese and German. This hugely limits the global reach and, consequently, transformative socioeconomic potential of such systems. In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. The tutorial will aim to provide answers or shed new light to the following questions: a) Why are multilingual dialogue systems so hard to build: what makes multilinguality for dialogue more challenging than for other NLP applications and tasks? b) What are the best existing methods and datasets for multilingual and cross-lingual (task-oriented) dialog systems? How are (multilingual) ToD systems usually evaluated? c) What are the promising future directions for multilingual ToD research: where can one draw inspiration from related NLP areas and tasks?
Adaptor: Objective-Centric Adaptation Framework for Language Models

Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks.This paper introduces the AdaptOr library 1 that transposes the traditional model-centric approach composed of pre-training + fine-tuning steps to objective-centric approach, composing the training process by applications of selected objectives.We survey research directions that can benefit from enhanced objective-centric experimentation in multi-task training, custom objectives development, dynamic training curricula, or domain adaptation.AdaptOr aims to ease the reproducibility of these research directions in practice.Finally, we demonstrate the practical applicability of AdaptOr in selected unsupervised domain adaptation scenarios."The measure of intelligence is the ability to change."-Albert Einstein
Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

Today, web content such as images, text, speeches, and videos are user-generated, and social networks have become increasingly popular as a means for people to share their ideas and opinions.One of the most popular social media for expressing their feelings towards events that occur is Twitter.The main objective of this study is to classify and analyze the content of the affiliates of the Pension and Funds Administration (AFP) published on Twitter.This study incorporates machine learning techniques for data mining, cleaning, tokenization, exploratory analysis, classification, and sentiment analysis.To apply the study and examine the data, Twitter was used with the hashtag #afp, followed by descriptive and exploratory analysis, including metrics of the tweets.Finally, a content analysis was carried out, including word frequency calculation, lemmatization, and classification of words by sentiment, emotions, and word cloud.The study uses tweets published in the month of May 2022.Sentiment distribution was also performed in three polarity classes: positive, neutral, and negative, representing 22%, 4%, and 74% respectively.Supported by the unsupervised learning method and the K-Means algorithm, we were able to determine the number of clusters using the elbow method.Finally, the sentiment analysis and the clusters formed indicate that there is a very pronounced dispersion, the distances are not very similar, even though the data standardization work was carried out.
Poirot at CMCL 2022 Shared Task: Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models

Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes.Different languages account for different cognitive triggers, however there seems to be some uniform indicators across languages.In this paper, we describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multilingual dataset.Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation for 2 main eye-tracking features.We train an end-to-end model to extract meaningful information from different languages and test our model on two separate datasets.We compare different transformer models and show ablation studies affecting model performance.Our final submission ranked 4th place for SubTask-1 and 1st place for SubTask-2 for the shared task.
Predicting Difficulty and Discrimination of Natural Language Questions

Item Response Theory (IRT) has been extensively used to numerically characterize question difficulty and discrimination for human subjects in domains including cognitive psychology and education (Primi et al., 2014;Downing, 2003).More recently, IRT has been used to similarly characterize item difficulty and discrimination for natural language models across various datasets (Lalor et al., 2019;Vania et al., 2021;Rodriguez et al., 2021).In this work, we explore predictive models for directly estimating and explaining these traits for natural language questions in a questionanswering context.We use HotpotQA for illustration.Our experiments show that it is possible to predict both difficulty and discrimination parameters for new questions, and these traits are correlated with features of questions, answers, and associated contexts.Our findings can have significant implications for the creation of new datasets and tests on the one hand and strategies such as active learning and curriculum learning on the other.