Miracle-dz/UniFAR-FTUs huggingface dataset:
Familiarity-Based Open-Set Recognition Under Adversarial Attacks

Open-set recognition (OSR), the identification of novel categories, can be a critical component when deploying classification models in real-world applications. Recent work has shown that familiarity-based scoring rules such as the Maximum Softmax Probability (MSP) or the Maximum Logit Score (MLS) are strong baselines when the closed-set accuracy is high. However, one of the potential weaknesses of familiarity-based OSR are adversarial attacks. Here, we present gradient-based adversarial attacks on familiarity scores for both types of attacks, False Familiarity and False Novelty attacks, and evaluate their effectiveness in informed and uninformed settings on TinyImageNet.
Toward 10 cm s−1 radial velocity accuracy on the Sun using a Fourier transform spectrometer

Abstract. The Institute for Astrophysics and Geophysics solar observatory is producing high-fidelity, ultra-high-resolution spectra (R > 500000) of the spatially resolved surface of the Sun using a Fourier transform spectrometer (FTS). The radial velocity (RV) calibration of these spectra is currently performed using absorption lines from Earth’s atmosphere, limiting the precision and accuracy. To improve the frequency calibration precision and accuracy, we use a Fabry–Pérot etalon (FP) setup that is an evolution of the CARMENES FP design and an iodine cell in combination. To create an accurate wavelength solution, the iodine cell is measured in parallel with the FP. The FP is then used to transfer the accurate wavelength solution provided by the iodine via a simultaneous calibration of solar observations. To verify the stability and precision of the FTS, we perform parallel measurements of the FP and an iodine cell. The measurements show an intrinsic stability of the FTS of a level of 1 m s − 1 over 90 h. The difference between the FP RVs and the iodine cell RVs show no significant trends during the same time span. The root mean square of the RV difference between the FP and iodine cell is 10.7 cm s − 1, which can be largely attributed to the intrinsic RV precisions of the iodine cell and the FP (10.2 and 1.0 cm s − 1, respectively). This shows that we can calibrate the FTS to a level of 10 cm s − 1, competitive with current state-of-the-art precision RV instruments. Based on these results, we argue that the spectrum of iodine can be used as an absolute reference to reach an RV accuracy of 10 cm s − 1.
A Bayesian framework for measuring association and its application to emotional dynamics in Web discourse

This paper introduces a Bayesian framework designed to measure the degree of association between categorical random variables. The method is grounded in the formal definition of variable independence and is implemented using Markov Chain Monte Carlo (MCMC) techniques. Unlike commonly employed techniques in Association Rule Learning, this approach enables a clear and precise estimation of confidence intervals and the statistical significance of the measured degree of association. We applied the method to non-exclusive emotions identified by annotators in 4,613 tweets written in Portuguese. This analysis revealed pairs of emotions that exhibit associations and mutually opposed pairs. Moreover, the method identifies hierarchical relations between categories, a feature observed in our data, and is utilized to cluster emotions into basic-level groups.
On Fefferman--Stein type inequality on Shilov boundaries and applications

In this paper, we establish the Fefferman--Stein type inequality for area integral and non-tangential maximal function on the Shilov boundary studied by Nagel and Stein in 2004. The technique here is inspired by Fefferman--Stein (1972) and Merryfield (1985) but we bypass the use of Fourier or group structure as these were not available on the polynomial domains of finite type. Direct applications include the maximal function characterisation of product Hardy space and the weak type endpoint estimate for product Calder\'on--Zygmund operators (such as the Cauchy--Szeg\H{o} projection) on the Shilov boundary.
ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP): Discovery of an Extremely Dense and Compact Object Embedded in the Prestellar Core G208.68-19.92-N2

The internal structure of the prestellar core G208.68-19.02-N2 (G208-N2) in the Orion Molecular Cloud 3 (OMC-3) region has been studied with the Atacama Large Millimeter/submillimeter Array. The dust continuum emission revealed a filamentary structure with a length of ∼5000 au and an average H2 volume density of ∼6 × 107 cm−3. At the tip of this filamentary structure, there is a compact object, which we call a nucleus, with a radius of ∼150–200 au and a mass of ∼0.1 M ⊙. The nucleus has a central density of ∼2 × 109 cm−3 with a radial density profile of r −1.87±0.11. The density scaling of the nucleus is ∼3.7 times higher than that of the singular isothermal sphere (SIS). This as well as the very low virial parameter of 0.39 suggests that the gravity is dominant over the pressure everywhere in the nucleus. However, there is no sign of CO outflow localized to this nucleus. The filamentary structure is traced by the N2D+ 3–2 emission, but not by the C18O 2–1 emission, implying the significant CO depletion due to high density and cold temperature. Toward the nucleus, the N2D+ also shows the signature of depletion. This could imply either the depletion of the parent molecule, N2, or the presence of the embedded very-low luminosity central source that could sublimate the CO in the very small area. The nucleus in G208-N2 is considered to be a prestellar core on the verge of first hydrostatic core (FHSC) formation or a candidate for the FHSC.
Unpaired MRI Super Resolution with Contrastive Learning

Magnetic resonance imaging (MRI) is crucial for enhancing diagnostic accuracy in clinical settings. However, the inherent long scan time of MRI restricts its widespread applicability. Deep learning-based image super-resolution (SR) methods exhibit promise in improving MRI resolution without additional cost. Due to lacking of aligned high-resolution (HR) and low-resolution (LR) MRI image pairs, unsupervised approaches are widely adopted for SR reconstruction with unpaired MRI images. However, these methods still require a substantial number of HR MRI images for training, which can be difficult to acquire. To this end, we propose an unpaired MRI SR approach that employs contrastive learning to enhance SR performance with limited HR training data. Empirical results presented in this study underscore significant enhancements in the peak signal-to-noise ratio and structural similarity index, even when a paucity of HR images is available. These findings accentuate the potential of our approach in addressing the challenge of limited HR training data, thereby contributing to the advancement of MRI in clinical applications.
IMM-UKF-TFS Model-based Approach for Intelligent Navigation

This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.
On the relationship between atomic oxygen and vertical shifts between OH Meinel bands originating from different vibrational levels

OH Meinel emissions from different vibrational levels are known to occur at slightly different altitudes in the terrestrial airglow. Earlier model studies suggested quenching by atomic oxygen to be a principal cause of these vertical shifts. Here we employ the tropical mesopause region—characterized by pronounced semiannual variations—as a natural laboratory to test the hypothesis that vertical shifts between different OH Meinel bands are a consequence of quenching by atomic oxygen. Nighttime satellite measurements of OH(3–1) and OH(6–2) volume emission rate profiles and atomic oxygen with Scanning Imaging Absorption Spectrometer for Atmospheric Chartography on Envisat are used for this purpose. Upper mesospheric atomic oxygen profiles are retrieved from measurements of the O(1S–1D) green line emission. The results demonstrate that vertical shifts between the OH bands investigated are indeed correlated with the amount of atomic oxygen in the upper mesosphere, corroborating the hypothesis.
Comparing Generic and Expert Models for Genre-Specific Text Simplification

We investigate how text genre influences the performance of models for controlled text simplification. Regarding datasets from Wikipedia and PubMed as two different genres, we compare the performance of genre-specific models trained by transfer learning and prompt-only GPT-like large language models. Our experiments showed that: (1) the performance loss of genre-specific models on general tasks can be limited to 2%, (2) transfer learning can improve performance on genre-specific datasets up to 10% in SARI score from the base model without transfer learning, (3) simplifications generated by the smaller but more customized models show similar performance in simplicity and a better meaning reservation capability to the larger generic models in both automatic and human evaluations.
CoPara: The First Dravidian Paragraph-level n-way Aligned Corpus

We present CoPara, the first publicly available paragraph-level (n-way aligned) multilingual parallel corpora for Dravidian languages. The collection contains 2856 paragraph/passage pairs between English and four Dravidian languages. We source the parallel paragraphs from the New India Samachar magazine and align them with English as a pivot language. We do human and artificial evaluations to validate the high-quality alignment and richness of the parallel paragraphs of a range of lengths. To show one of the many ways this dataset can be wielded, we finetuned IndicBART, a seq2seq NMT model on all XX-En pairs of languages in CoPara which perform better than existing sentence-level models on standard benchmarks (like BLEU) on sentence level translations and longer text too. We show how this dataset can enrich a model trained for a task like this, with more contextual cues and beyond sentence understanding even in low-resource settings like that of Dravidian languages. Finally, the dataset and models are made available publicly at CoPara to help advance research in Dravidian NLP, parallel multilingual, and beyond sentence-level tasks like NMT, etc.
Coherent Story Generation with Structured Knowledge

The emergence of pre-trained language models has taken story generation, which is the task of automatically generating a comprehensible story from limited information, to a new stage. Although generated stories from the language models are fluent and grammatically correct, the lack of coherence affects their quality. We propose a knowledge-based multi-stage model that incorporates the schema, a kind of structured knowledge, to guide coherent story generation. Our framework includes a schema acquisition module, a plot generation module, and a surface realization module. In the schema acquisition module, high-relevant structured knowledge pieces are selected as a schema. In the plot generation module, a coherent plot plan is navigated by the schema. In the surface realization module, conditioned by the generated plot, a story is generated. Evaluations show that our methods can generate more comprehensible stories than strong baselines, especially with higher global coherence and less repetition.
Controllable Active-Passive Voice Generation using Prefix Tuning

The prompting paradigm is an uprising trend in the field of Natural Language Processing (NLP) that aims to learn tasks by finding appropriate prompts rather than fine-tuning the model weights. Such prompts can express an intention, e.g., they can instruct a language model to generate a summary of a given event. In this paper, we study how to influence (”control”) the language generation process such that the outcome fulfills a requested linguistic property. More specifically, we look at controllable active-passive (AP) voice generation, i.e., we require the model to generate a sentence in the requested voice. We build upon the prefix tuning approach and introduce control tokens that are trained on controllable AP generation. We create an AP subset of the WebNLG dataset to fine-tune these control tokens. Among four different models, the one trained with a contrastive learning approach yields the best results in terms of AP accuracy ( 95%) but at the cost of decreased performance on the original WebNLG task.
Training and Evaluation of Named Entity Recognition Models for Classical Latin

We evaluate the performance of various models on the task of named entity recognition (NER) for classical Latin. Using an existing dataset, we train two transformer-based LatinBERT models and one shallow conditional random field (CRF) model. The performance is assessed using both standard metrics and a detailed manual error analysis, and compared to the results obtained by different already released Latin NER tools. Both analyses demonstrate that the BERT models achieve a better f1-score than the other models. Furthermore, we annotate new, unseen data for further evaluation of the models, and we discuss the impact of annotation choices on the results.
The 2023 ReproNLP Shared Task on Reproducibility of Evaluations in NLP: Overview and Results

This paper presents an overview of, and the results from, the 2023 Shared Task on Reproducibility of Evaluations in NLP (ReproNLP’23), following on from two previous shared tasks on reproducibility of evaluations in NLG, ReproGen’21 and ReproGen’22. This shared task series forms part of an ongoing research programme designed to develop theory and practice of reproducibility assessment in NLP and machine learning, all against a background of an interest in reproducibility that con- tinues to grow in the two fields. This paper describes the ReproNLP’23 shared task, summarises results from the reproduction studies submitted, and provides comparative analysis of the results.
Modeling Easiness for Training Transformers with Curriculum Learning

Directly learning from complex examples is generally problematic for humans and machines. Indeed, a better strategy is exposing learners to examples in a reasonable, pedagogically-motivated order. Curriculum Learning (CL) has been proposed to import this strategy when training machine learning models. In this paper, building on Curriculum Learning, we propose a novel, linguistically motivated measure to determine example complexity for organizing examples during learning. Our complexity measure - LRC- is based on length, rarity, and comprehensibility. Our resulting learning model is CL-LRC, that is, CL with LRC. Experiments on downstream tasks show that CL-LRC outperforms existing CL and non-CL methods for training BERT and RoBERTa from scratch. Furthermore, we analyzed different measures, including perplexity, loss, and learning curve of different models pre-trained from scratch, showing that CL-LRC performs better than the state-of-the-art.
KEC_AI_NLP@DravidianLangTech: Abusive Comment Detection in Tamil Language

Our work aims to identify the negative comments that is associated with Counter-speech,Xenophobia, Homophobia,Transphobia, Misandry, Misogyny, None-of-the-above categories, In order to identify these categories from the given dataset, we propose three different models such as traditional machine learning techniques, deep learning model and transfer Learning model called BERT is also used to analyze the texts. In the Tamil dataset, we are training the models with Train dataset and test the models with Validation data. Our Team Participated in the shared task organised by DravidianLangTech and secured 4th rank in the task of abusive comment detection in Tamil with a macro- f1 score of 0.35. Also, our run was submitted for abusive comment detection in code-mixed languages (Tamil-English) and secured 6th rank with a macro-f1 score of 0.42.
A Joint Model of Automatic Word Segmentation and Part-Of-Speech Tagging for Ancient Classical Texts Based on Radicals

The digitization of ancient books necessitates the implementation of automatic word segmentation and part-of-speech tagging. However, the existing research on this topic encounters pressing issues, including suboptimal efficiency and precision, which require immediate resolution. This study employs a methodology that combines word segmentation and part-of-speech tagging. It establishes a correlation between fonts and radicals, trains the Radical2Vec radical vector representation model, and integrates it with the SikuRoBERTa word vector representation model. Finally, it connects the BiLSTM-CRF neural network.The study investigates the combination of word segmentation and part-of-speech tagging through an experimental approach using a specific data set. In the evaluation dataset, the F1 score for word segmentation is 95.75%, indicating a high level of accuracy. Similarly, the F1 score for part-of-speech tagging is 91.65%, suggesting a satisfactory performance in this task. This model enhances the efficiency and precision of the processing of ancient books, thereby facilitating the advancement of digitization efforts for ancient books and ensuring the preservation and advancement of ancient book heritage.
A Research-Based Guide for the Creation and Deployment of a Low-Resource Machine Translation System

The machine translation (MT) field seems to focus heavily on English and other high-resource languages. Though, low-resource MT (LRMT) is receiving more attention than in the past. Successful LRMT systems (LRMTS) should make a compelling business case in terms of demand, cost and quality in order to be viable for end users. When used by communities where low-resource languages are spoken, LRMT quality should not only be determined by the use of traditional metrics like BLEU, but it should also take into account other factors in order to be inclusive and not risk overall rejection by the community. MT systems based on neural methods tend to perform better with high volumes of training data, but they may be unrealistic and even harmful for LRMT. It is obvious that for research purposes, the development and creation of LRMTS is necessary. However, in this article, we argue that two main workarounds could be considered by companies that are considering deployment of LRMTS in the wild: human-in-the-loop and sub-domains.
Advancing Topical Text Classification: A Novel Distance-Based Method with Contextual Embeddings

This study introduces a new method for distance-based unsupervised topical text classification using contextual embeddings. The method applies and tailors sentence embeddings for distance-based topical text classification. This is achieved by leveraging the semantic similarity between topic labels and text content, and reinforcing the relationship between them in a shared semantic space. The proposed method outperforms a wide range of existing sentence embeddings on average by 35%. Presenting an alternative to the commonly used transformer-based zero-shot general-purpose classifiers for multiclass text classification, the method demonstrates significant advantages in terms of computational efficiency and flexibility, while maintaining comparable or improved classification results.
A Reproduction Study of the Human Evaluation of Role-Oriented Dialogue Summarization Models

This paper reports a reproduction study of the human evaluation of role-oriented dialogue summarization models, as part of the ReproNLP Shared Task 2023 on Reproducibility of Evaluations in NLP. We outline the disparities between the original study’s experimental design and our reproduction study, along with the outcomes obtained. The inter-annotator agreement within the reproduction study is observed to be lower, measuring 0.40 as compared to the original study’s 0.48. Among the six conclusions drawn in the original study, four are validated in our reproduction study. We confirm the effectiveness of the proposed approach on the overall metric, albeit with slightly poorer relative performance compared to the original study. Furthermore, we raise an open-ended inquiry: how can subjective practices in the original study be identified and addressed when conducting reproduction studies?
Bringing Mediterranean agriculture into the theoretical debates

This chapter aims to analyse the evolution of competing paradigms and theoretical frameworks that have pervaded the debates on the present and future of agricultural and food systems and their associated rural areas. From this global overview, we will extract common features of paradigms that are being reproduced over time as well as highlight the innovations introduced. Particular attention will be paid to discuss the responses and contributions inspired by European Mediterranean-based research, setting up the framework that underlines the subsequent chapters of the volume.
Reproduction of Human Evaluations in: “It’s not Rocket Science: Interpreting Figurative Language in Narratives”

We describe in this paper an attempt to reproduce some of the human of evaluation results from the paper “It’s not Rocket Science: Interpreting Figurative Language in Narratives”. In particular, we describe the methodology used to reproduce the chosen human evaluation, the challenges faced, and the results that were gathered. We will also make some recommendations on the learnings obtained from this reproduction attempt and what improvements are needed to enable more robust reproductions of future NLP human evaluations.
IIITDWD@LT-EDI-2023 Unveiling Depression: Using pre-trained language models for Harnessing Domain-Specific Features and Context Information

Depression has become a common health problem impacting millions of individuals globally. Workplace stress and an unhealthy lifestyle have increased in recent years, leading to an increase in the number of people experiencing depressive symptoms. The spread of the epidemic has further exacerbated the problem. Early detection and precise prediction of depression are critical for early intervention and support for individuals at risk. However, due to the social stigma associated with the illness, many people are afraid to consult healthcare specialists, making early detection practically impossible. As a result, alternative strategies for depression prediction are being investigated, one of which is analyzing users’ social media posting behaviour. The organizers of LT-EDI@RANLP carried out a shared Task to encourage research in this area. Our team participated in the shared task and secured 21st rank with a macro F1 score 0f 0.36. This article provides a summary of the model presented in the shared task.