Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Characterization of Performance Variability for OpenMP Runtime

In the high performance computing (HPC) domain, performance variability is a major scalability issue for parallel computing applications with heavy synchronization and communication. In this paper, we present an experimental performance analysis of OpenMP benchmarks regarding the variation of execution time, and determine the potential factors causing performance variability. Our work offers some understanding of performance distributions and directions for future work on how to mitigate variability for OpenMP-based applications. Two representative OpenMP benchmarks from the EPCC OpenMP micro-benchmark suite and BabelStream are run across two x86 multicore platforms featuring up to 256 threads. From the obtained results, we characterize and explain the execution time variability as a function of thread-pinning, simultaneous multithreading (SMT) and core frequency variation.
A Local Parallel Finite Element Method for Super-Hydrophobic Proppants in a Hydraulic Fracturing System Based on a 2D/3D Transient Triple-Porosity Navier-Stokes Model

A hydraulic fracturing system with super-hydrophobic proppants is characterized by a transient triple-porosity Navier-Stokes model. For this complex multiphysics system, particularly in the context of three-dimensional space, a local parallel and non-iterative finite element method based on two-grid discretizations is proposed. The underlying idea behind utilizing the local parallel approach is to combine a decoupled method, a two-grid method and a domain decomposition method. The strategy allows us to initially capture low-frequency data across the decoupled domain using a coarse grid. Then it tackles high-frequency components by solving residual equations within overlapping subdomains by employing finer grids and local parallel procedures at each time step. By utilizing this approach, a significant improvement in computational efficiency can be achieved. Furthermore, the convergence results of the approximate solutions from the algorithm are obtained. Finally, we perform 2D/3D numerical experiments to demonstrate the effectiveness and efficiency of the algorithm as well as to illustrate its advantages in application.
2023 Sparse PCA Beyond Covariance Thresholding *

In the Wishart model for sparse PCA we are given 𝑛 samples 𝒀 1 , . , 𝒀 𝑛 drawn independently from a 𝑑 -dimensional Gaussian distribution 𝑁 ( 0 , Id + 𝛽 𝑣𝑣 ⊤ ) , where 𝛽 > 0 and 𝑣 ∈ ℝ 𝑑 is a 𝑘 - sparse unit vector, and we wish to recover 𝑣 (up to sign). We show that if 𝑛 > Ω ( 𝑑 ) , then for every 𝑡 ≪ 𝑘 there exists an algorithm running in time 𝑛 · 𝑑 𝑂 ( 𝑡 ) that solves this problem as long as
Small-number statistics, common sense, and profit: challenges and non-challenges for hurricane forecasting

When making only one forecast per year, or per decade, it can take some time to establish statistical confidence in the skill of a given forecast scheme. Must a risk tolerant decision maker wait decades until skill is “proven” if that decision maker believes the system to have value? What of a risk neutral decision maker? A methodology is illustrated to demonstrate there are imperfect forecast systems which almost certainly have nontrivial value long before one might establish that their skill was statistically significant. 1 Identifying skill with small datasets Number of Atlantic Basin hurricanes 1960-2009 196
Interns@LT-EDI : Detecting Signs of Depression from Social Media Text

This submission presents our approach for depression detection in social media text. The methodology includes data collection, preprocessing - SMOTE, feature extraction/selection - TF-IDF and Glove, model development- SVM, CNN and Bi-LSTM, training, evaluation, optimisation, and validation. The proposed methodology aims to contribute to the accurate detection of depression.
Three Approaches to Client Email Topic Classification

This paper describes a use case that was implemented and is currently running in production at the Nova Ljubljanska Banka, that involves classifying incoming client emails in the Slovenian language according to their topics and priorities. Since the proposed approach relies only on the Named Entity Recogniser (NER) of personal names as a language-dependent resource (for the purpose of anonymisation), that is the only prerequisite for applying the approach to any other language.
Vector Based Stylistic Analysis on Ancient Chinese Books: Take the Three Commentaries on the Spring and Autumn Annals as an Example

Commentary of Gongyang, Commentary of Guliang, and Commentary of Zuo are collectively called the Three Commentaries on the Spring and Autumn Annals, which are the supplement and interpretation of the content of Spring and Autumn Annals with value in historical and literary research. In traditional research paradigms, scholars often explored the differences between the Three Commentaries within the details in contexts. Starting from the view of computational humanities, this paper examines the differences in the language style of the Three Commentaries through the representation of language, which takes the methods of deep learning. Specifically, this study vectorizes the context at word and sentence levels. It maps them into the same plane to find the differences between the use of words and sentences in the Three Commentaries. The results show that the Commentary of Gongyang and the Commentary of Guliang are relatively similar, while the Commentary of Zuo is significantly different. This paper verifies the feasibility of deep learning methods in stylistics study under computational humanities. It provides a valuable perspective for studying the Three Commentaries on the Spring and Autumn Annals.
Crowdsourcing Veridicality Annotations in Spanish: Can Speakers Actually Agree?

In veridicality studies, an area of research of Natural Language Inference (NLI), the factuality of different contexts is evaluated. This task, known to be a difficult one since often it is not clear what the interpretation should be Uma et al. (2021), is key for building any Natural Language Understanding (NLU) system that aims at making the right inferences. Here the results of a study that analyzes the veridicality of mood alternation and specificity in Spanish, and whose labels are based on those of Saurí and Pustejovsky (2009) are presented. It has an inter-annotator agreement of AC2 = 0.114, considerably lower than that of de Marneffe et al. (2012) (κ = 0.53), a main reference to this work; and a couple of mood-related significant effects. Due to this strong lack of agreement, an analysis of what factors cause disagreement is presented together with a discussion based on the work of de Marneffe et al. (2012) and Pavlick and Kwiatkowski (2019) about the quality of the annotations gathered and whether other types of analysis like entropy distribution could better represent this corpus. The annotations collected are available at https://github.com/narhim/veridicality_spanish.
ML&AI_IIITRanchi@LT-EDI-2023: Identification of Hope Speech of YouTube comments in Mixed Languages

Hope speech analysis refers to the examination and evaluation of speeches or messages that aim to instill hope, inspire optimism, and motivate individuals or communities. It involves analyzing the content, language, rhetorical devices, and delivery techniques used in a speech to understand how it conveys hope and its potential impact on the audience. The objective of this study is to classify the given text comments as Hope Speech or Not Hope Speech. The provided dataset consists of YouTube comments in four languages: English, Hindi, Spanish, Bulgarian; with pre-defined classifications. Our approach involved pre-processing the dataset and using the TF-IDF (Term Frequency-Inverse Document Frequency) method.
Mind the User! Measures to More Accurately Evaluate the Practical Value of Active Learning Strategies

One solution to limited annotation budgets is active learning (AL), a collaborative process of human and machine to strategically select a small but informative set of examples. While current measures optimize AL from a pure machine learning perspective, we argue that for a successful transfer into practice, additional criteria must target the second pillar of AL, the human annotator. In text classification, e.g., where practitioners regularly encounter datasets with an increased number of imbalanced classes, measures like F1 fall short when finding all classes or identifying rare cases is required. We therefore introduce four measures that reflect class-related demands that users place on data acquisition. In a comprehensive comparison of uncertainty-based, diversity-based, and hybrid query strategies on six different datasets, we find that strong F1 performance is not necessarily associated with full class coverage. Uncertainty sampling outperforms diversity sampling in selecting minority classes and covering classes more efficiently, while diversity sampling excels in selecting less monotonous batches. Our empirical findings emphasize that a holistic view is essential when evaluating AL approaches to ensure their usefulness in practice - the actual, but often overlooked, goal of development. To this end, standard measures for assessing the performance of text classification need to be complemented by such that more appropriately reflect user needs.
Reproducing a Comparative Evaluation of German Text-to-Speech Systems

This paper describes the reproduction of a human evaluation in Language-Agnostic Meta- Learning for Low-Resource Text-to-Speech with Articulatory Features reported in Lux and Vu (2022). It is a contribution to the ReproNLP 2023 Shared Task on Reproducibility of Evaluations in NLP. The original evaluation assessed the naturalness of audio generated by different Text-to-Speech (TTS) systems for German, and our goal was to repeat the experiment with a different set of evaluators. We reproduced the evaluation based on data and instructions provided by the original authors, with some uncertainty concerning the randomisation of question order. Evaluators were recruited via email to relevant mailing lists and we received 157 responses over the course of three weeks. Our initial results show low reproducibility, but when we assume that the systems of the original and repeat evaluation experiment have been transposed, the reproducibility assessment improves markedly. We do not know if and at what point such a transposition happened; however, an initial analysis of our audio and video files provides some evidence that the system assignment in our repeat experiment is correct.
Same Trends, Different Answers: Insights from a Replication Study of Human Plausibility Judgments on Narrative Continuations

We reproduced the human-based evaluation of the continuation of narratives task presented by Chakrabarty et al. (2022). This experiment is performed as part of the ReproNLP Shared Task on Reproducibility of Evaluations in NLP (Track C). Our main goal is to reproduce the original study under conditions as similar as possible. Specifically, we follow the original experimental design and perform human evaluations of the data from the original study, while describing the differences between the two studies. We then present the results of these two studies together with an analysis of similarities between them. Inter-annotator agreement (Krippendorff’s alpha) in the reproduction study is lower than in the original study, while the human evaluation results of both studies have the same trends, that is, our results support the findings in the original study.
SSSD: Leveraging Pre-trained Models and Semantic Search for Semi-supervised Stance Detection

Pre-trained models (PTMs) based on the Transformers architecture are trained on massive amounts of data and can capture nuances and complexities in linguistic expressions, making them a powerful tool for many natural language processing tasks. In this paper, we present SSSD (Semantic Similarity Stance Detection), a semi-supervised method for stance detection on Twitter that automatically labels a large, domain-related corpus for training a stance classification model. The method assumes as input a domain set of tweets about a given target and a labeled query set of tweets of representative arguments related to the stances. It scales the automatic labeling of a large number of tweets, and improves classification accuracy by leveraging the power of PTMs and semantic search to capture context and meaning. We largely outperformed all baselines in experiments using the Semeval benchmark.
Semantic Specifics of Bulgarian Verbal Computer Terms

This paper represents a description of Bulgarian verbal computer terms with a view to the specifics of their translation in English. The study employs a subset of 100 verbs extracted from the Bulgarian WordNet (BulNet) and from the internet. The analysis of their syntactic and semantic structure is a part of a study of the general lexis of Bulgarian. The aim of the paper is to (1) identify some problem areas of the description and translation of general lexis verbs, (2) offer an approach to the semantic description of metaphor-based terms from the perspective of Frame Semantics; (3) raise questions about the definition of general lexis with respect to Bulgarian and across languages.
On the Development of Interlinearized Ancient Literature of Ethnic Minorities: A Case Study of the Interlinearization of Ancient Written Tibetan Literature

Ancient ethnic documents are essential to China’s ancient literature and an indispensable civilizational achievement of Chinese culture. However, few research teams are involved due to language and script literacy limitations. To address these issues, this paper proposes an interlinearized annotation strategy for ancient ethnic literature. This strategy aims to alleviate text literacy difficulties, encourage interdisciplinary researchers to participate in studying ancient ethnic literature, and improve the efficiency of ancient ethnic literature development. Concretely, the interlinearized annotation consists of original, word segmentation, Latin, annotated, and translation lines. In this paper, we take ancient Tibetan literature as an example to explore the interlinearized annotation strategy. However, manually building large-scale corpus is challenging. To build a large-scale interlinearized dataset, we propose a multi-task learning-based interlinearized annotation method, which can generate interlinearized annotation lines based on the original line. Experimental results show that after training on about 10,000 sentences (lines) of data, our model achieves 70.9% and 63.2% F1 values on the segmentation lines and annotated lines, respectively, and 18.7% BLEU on the translation lines. It dramatically enhances the efficiency of data annotation, effectively speeds up interlinearized annotation, and reduces the workload of manual annotation.
Improving Translation Quality for Low-Resource Inuktitut with Various Preprocessing Techniques

Neural machine translation has been shown to outperform all other machine translation paradigms when trained in a high-resource setting. However, it still performs poorly when dealing with low-resource languages, for which parallel data for training is scarce. This is especially the case for morphologically complex languages such as Turkish, Tamil, Uyghur, etc. In this paper, we investigate various preprocessing methods for Inuktitut, a low-resource indigenous language from North America, without a morphological analyzer. On both the original and romanized scripts, we test various preprocessing techniques such as Byte-Pair Encoding, random stemming, and data augmentation using Hungarian for the Inuktitut-to-English translation task. We found that there are benefits to retaining the original script as it helps to achieve higher BLEU scores than the romanized models.
huPWKP: A Hungarian Text Simplification Corpus

In this article we introduce huPWKP, the first parallel corpus consisting of Hungarian standard language-simplified sentence pairs. As Hungarian is a quite low-resource language in regards to text simplification, we opted for translating an already existing corpus, PWKP (Zhu et al., 2010), on which we performed some cleaning in order to improve its quality. We evaluated the corpus both with the help of human evaluators and by training a seq2seq model on both the Hungarian corpus and the original (cleaned) English corpus. The Hungarian model performed slightly worse in terms of automatic metrics; however, the English model attains a SARI score close to the state of the art on the official PWKP set. According to the human evaluation, the corpus performs at around 3 on a scale ranging from 1 to 5 in terms of information retention and increase in simplification and around 3.7 in terms of grammaticality.
JudithJeyafreeda@LT-EDI-2023: Using GPT model for recognition of Homophobia/Transphobia detection from social media

Homophobia and Transphobia is defined as hatred or discomfort towards Gay, Lesbian, Transgender or Bisexual people. With the increase in social media, communication has become free and easy. This also means that people can also express hatred and discomfort towards others. Studies have shown that these can cause mental health issues. Thus detection and masking/removal of these comments from the social media platforms can help with understanding and improving the mental health of LGBTQ+ people. In this paper, GPT2 is used to detect homophobic and/or transphobic comments in social media comments. The comments used in this paper are from five (English, Spanish, Tamil, Malayalam and Hindi) languages. The results show that detecting comments in English language is easier when compared to the other languages.
Data Fusion for Better Fake Reviews Detection

Online reviews have become critical in informing purchasing decisions, making the detection of fake reviews a crucial challenge to tackle. Many different Machine Learning based solutions have been proposed, using various data representations such as n-grams or document embeddings. In this paper, we first explore the effectiveness of different data representations, including emotion, document embedding, n-grams, and noun phrases in embedding for mat, for fake reviews detection. We evaluate these representations with various state-of-the-art deep learning models, such as BILSTM, LSTM, GRU, CNN, and MLP. Following this, we propose to incorporate different data repre- sentations and classification models using early and late data fusion techniques in order to im- prove the prediction performance. The experiments are conducted on four datasets: Hotel, Restaurant, Amazon, and Yelp. The results demonstrate that combination of different data representations significantly outperform any of the single data representations
Evaluating Unsupervised Hierarchical Topic Models Using a Labeled Dataset

Topic modeling is a commonly used method for identifying and extracting topics from a corpus of documents. While several evaluation techniques, such as perplexity and topic coherence, have been developed to assess the quality of extracted topics, they fail to determine whether all topics have been identified and to what extent they have been represented. Additionally, hierarchical topic models have been proposed, but the quality of the hierarchy produced has not been adequately evaluated. This study proposes a novel approach to evaluating topic models that supplements existing methods. Using a labeled dataset, we trained hierarchical topic models in an unsupervised manner and used the known labels to evaluate the accuracy of the results. Our findings indicate that labels encompassing a substantial number of documents achieve high accuracy of over 70%. Although there are 90 labels in the dataset, labels that cover only 1% of the data still achieve an average accuracy of 37.9%, demonstrating the effectiveness of hierarchical topic models even on smaller subsets. Furthermore, we demonstrate that these labels can be used to assess the quality of the topic tree and confirm that hierarchical topic models produce coherent taxonomies for the labels.
Leveraging Probabilistic Graph Models in Nested Named Entity Recognition for Polish

This paper presents ongoing work on leveraging probabilistic graph models, specifically conditional random fields and hidden Markov models, in nested named entity recognition for the Polish language. NER is a crucial task in natural language processing that involves identifying and classifying named entities in text documents. Nested NER deals with recognizing hierarchical structures of entities that overlap with one another, presenting additional challenges. The paper discusses the methodologies and approaches used in nested NER, focusing on CRF and HMM. Related works and their contributions are reviewed, and experiments using the KPWr dataset are conducted, particularly with the BiLSTM-CRF model and Word2Vec and HerBERT embeddings. The results show promise in addressing nested NER for Polish, but further research is needed to develop robust and accurate models for this complex task.
Taxonomy-Based Automation of Prior Approval Using Clinical Guidelines

Performing prior authorization on patients in a medical facility is a time-consuming and challenging task for insurance companies. Automating the clinical decisions that lead to authorization can reduce the time that staff spend executing such procedures. To better facilitate such critical decision making, we present an automated approach to predict one of the challenging tasks in the process called primary clinical indicator prediction, which is the outcome of this procedure. The proposed solution is to create a taxonomy to capture the main categories in primary clinical indicators. Our approach involves an important step of selecting what is known as the “primary indicator” – one of the several heuristics based on clinical guidelines that are published and publicly available. A taxonomy based PI classification system was created to help in the recognition of PIs from free text in electronic health records (EHRs). This taxonomy includes comprehensive explanations of each PI, as well as examples of free text that could be used to detect each PI. The major contribution of this work is to introduce a taxonomy created by three professional nurses with many years of experience. We experiment with several state-of-the-art supervised and unsupervised techniques with a focus on prior approval for spinal imaging. The results indicate that the proposed taxonomy is capable of increasing the performance of unsupervised approaches by up to 10 F1 points. Further, in the supervised setting, we achieve an F1 score of 0.61 using a conventional technique based on term frequency–inverse document frequency that outperforms other deep-learning approaches.
Impact of Emojis on Automatic Analysis of Individual Emotion Categories

Automatic emotion analysis is a highly challenging task for Natural Language Processing, which has so far mainly relied on textual contents to determine the emotion of text. However, words are not the only media that carry emotional information. In social media, people also use emojis to convey their feelings. Recently, researchers have studied emotional aspects of emojis, and use emoji information to improve the emotion detection and classification, but many issues remain to be addressed. In this study, we examine the impact of emoji embedding on emotion classification and intensity prediction on four individual emotion categories, including anger, fear, joy, and sadness, in order to investigate how emojis affect the automatic analysis of individual emotion categories and intensity. We conducted a comparative study by testing five machine learning models with and without emoji embeddings involved. Our experiment demonstrates that emojis have varying impact on different emotion categories, and there is potential that emojis can be used to enhance emotion information processing.