Miracle-dz/UniFAR-FTUs huggingface dataset:
SEUNIPD@CLEF: Team JIHUMING on Enhancing Search Engine Performance with Character N-Grams, Query Expansion, and Named Entity Recognition

Our group will propose a novel search engine for the Longitudinal Evaluation of Model Performance (LongEval) task at CLEF 2023 [1]; it will also be the final work of the subject Search Engines at the University of Padova. Our system focuses on the short-term and long-term temporal persistence of the systems’ performance, for a collection of both English and French documents. Our approach involves considering both English and French versions of the documents using whitespace tokenization, stopword removal and stemming. We generate character N-grams to identify recurring word structures (as prefixes or suffixes) repeated over documents. We also use query expansion with synonyms (in English) and some Natural Language Processing (NLP) techniques as Named Entity Recognition (NER) to further refine our system. The similarity function utilized in our approach is BM25. Our system was developed in Java and primarily utilized the Lucene library. After extensive experiments on these techniques, we came up with five systems that have produced the best results in terms of MAP and NDCG scores. We analyzed these five selected systems by examining their MAP, NDCG, and Rprec scores on the test data. Additionally, we performed a Two-Way ANOVA to assess the AP of these systems. To compare our systems with each other, we will utilize the Tukey Honestly Significant Difference (HSD) test. In summary, our analysis indicates that incorporating French queries enhances search results, larger N-gram sizes contribute to improved effectiveness, while our NER approach negatively affects the scores.
AUEB NLP Group at ImageCLEFmed Caption 2022

We present the methods AUEB’s NLP Group used to participate in the annual ImageCLEFmedical Caption Task. The task comprises of the Concept Detection and the Caption Prediction sub-tasks. Concept Detection aims to automatically tag medical images with relevant medical concepts, while Caption Prediction generates draft diagnostic captions of medical images to help medical experts prepare diagnostic reports. For Concept Detection, we employ CNN image encoders, combined with a feed-forward neural network classifier or a retrieval module, extending our previous work. For Caption Prediction, we also extend the retrieval approach of our previous work with a caption selection method; we also experiment with a state-of-the-art memory-enhanced caption generation method, a simpler CNN-RNN caption generation model, and a captions ensemble method, which combines predictions from our different models. We ranked 1st in Concept Detection and 2nd in Caption Prediction.
Survival Analysis for Multiple Sclerosis: Predicting Risk of Disease Worsening

Multiple sclerosis (MS) is a chronic neurodegenerative disease with a wide range of clinical manifestations and disease courses. Prognosis prediction is therefore an important tool for clinical decision-making and treatment administration. As proposed by the iDPP (cid:17) CLEF 2023 challenge, we have explored several survival prediction models to rank MS patients according to the risk of worsening. Two definitions of disease worsening were explored, with subtask (a) using a fixed EDSS threshold and subtask (b) considering a baseline-dependent threshold. The models were trained using demographic and clinical data collected at diagnosis and available EDSS and relapse follow-up records. The temporal nature of the latter was addressed by computing feature statistics. Model performance was evaluated through c-index. In Task 1a, the Random Survival Forest model reached 0.801 (95% CI [0.678; 0.924]) in the private test set, while in Task 1b, the Fast Kernel SVM model reached 0.690 (95% CI [0.591; 0.788]).
Transferring Pre-Trained Large Language-Image Model for Medical Image Captioning

This paper introduces the work conducted by the team "closeAI2023" in the ImageCLEFmedical Caption 2023 Image Caption sub-task. Medical image captioning poses unique difficulties due to the specialized nature of the medical domain. It requires the generation of accurate and coherent captions that not only describe the visual content but also capture the essential medical information conveyed by the images. To leverage the abilities of pre-trained Large-Image Models, we utilise the state-of-the-art BLIP-2 with a giant vision transformer (ViT-g) and Open Pre-trained Transformer Language Models (OPT 2 . 7 𝐵 ) as the foundation of our caption prediction sub-task. To adapt the model to the medical domain, we employed a two-stage fine-tuning process. The pre-trained OPT 2 . 7 𝐵 was fixed during the whole training process. A step-wise fine-tuning of the ViT-g and the Q-Former modules was conducted to better align with the characteristics of medical data. Our team’s approach yielded promising results, as we achieved a second-place ranking among all participating teams with a BERTScore of 0.6281. Additionally, our model performed well across various evaluation metrics: ROUGE of 0.2401 (4th), BLEURT of 0.3209 (1st), BLEU of 0.1846 (3rd), METEOR of 0.0873 (3rd), CIDEr of 0.2377 (1st), and CLIPScore of 0.8074 (3rd).
LSTM-Attention Architecture for Online Bilingual Sexism Detection

The paper describes the results submitted by ‘Team-SMS’ at EXIST 2023. A dataset of 6920 tweets for training, 1038 for validation, and 2076 tweets for testing was provided by the task organizers to train and test our models. Our models include LSTM models coupled with attention layers and without attention. For calculation of soft scores according to the task we tried to mimic human performance by taking an average of different machine learning model predictions using Multinomial Naive Bayes, Linear Support Vector Classifier, Multi Layer Perceptron, XGBoost, LSTM using GloVe embeddings, and LSTM using fastText embeddings. We discuss our approach to remove the ambiguity in the labeling process and detailed description of our work.
Optimizing Fine-Grained Fungi Classification for Diverse Application-Oriented Open-Set Metrics

Fine-grained fungi species classification is an important task to support distinguishing edible and poisonous fungi and thus, reducing the risk of accidental poisoning. Therefore, the FungiCLEF 2023 challenge seeks to find the best solution for this task considering multiple metrics with each having a different application in focus like e.g., a low confusion of edible and poisonous fungi. We propose a method to approach the different metrics by exploiting modern deep learning networks, strong data augmentation and class-balanced training. The challenge assumes an open-set scenario which includes unknown classes during evaluation which we identify by a confidence thresholding approach. With our method, we achieved the 2 nd place in the challenge with good scores across all metrics. Code is available at: https://github.com/wolfstefan/fungi2023.
IUST_NLPLAB at ImageCLEFmedical Caption Tasks 2023

We present models implemented by the IUST_NLPLAB group for ImageCLEFmedical Caption Task 2023. This task contains two subtasks: Concept Detection and Caption Prediction. Under the first subtask, the model should extract medical concepts contained in radiology images. These concepts can be used for context-based image and information retrieval. Under the second subtask, the model predicts the caption for a medical image. This can be used for improving the diagnosis and treatment of diseases by saving time, money and helping physicians. This was our second experience to participate in this competition. We used diffrent models for both subtasks. We were able to get the 4th rank in the concepts detection subtask with a score of 0.49. Also, in the caption prediction subtask, we were able to get the 12th rank based on the BERTScore evaluation metric. This is despite the fact that our model has won the first rank based on ROUGE, BLEU and METEOR. From this, it can be concluded that the type of evaluation metric determined has an important effect on the results of this subtask.
SEASONAL VARIATIONS IN PHYSICO-CHEMICAL CHARACTERISTICS OF AGNIAR ESTUARY SOUTHEAST COAST OF INDIA

The seasonal variations of physico-chemical parameters were studied during October 2011 to September 2012 in the Agniar estuary (Lat.10° 20' N Long.79° 23’E ) Adirampattinam southeast coast of India. The rain fall ranged between 0.33 to 551.8. The atmospheric and water temperature ranged from 27.5°C to 35°C and 25°C to 31.5°C respectively. The pH ranged between 7.1 and 8.2 . The DO was ranged from 3.5 to 7.2 ml/l. Salinity fluctuated between 5.5‰ and 34‰. The nutrients such as total phosphorus, nitrate and silicate ranged from0.29 to 2.15 µg/l, 0.47 to 3.75 µg/l and 28.25 to 98.74 µg/l respectively. The DO and nutrients was found to be low in summer and high during monsoon season. Similarly temperature, pH and salinity was low during monsoon and high during summer season.
Overview of the CLEF-2023 CheckThat! Lab Task 1 on Check-Worthiness in Multimodal and Multigenre Content

We present an overview of CheckThat! Lab’s 2023 Task 1, which is part of CLEF-2023. Task 1 asks to determine whether a text item, or a text coupled with an image, is check-worthy. This task places a special emphasis on COVID-19, political debates and transcriptions, and it is conducted in three languages: Arabic, English, and Spanish. A total of 15 teams participated, and most submissions managed to achieve significant improvements over the baselines using Transformer-based models. Out of these, seven teams participated in the multimodal subtask (1A), and 12 teams participated in the Multigenre subtask (1B), collectively submitting 155 official runs for both subtasks. Across both subtasks, approaches that targeted multiple languages, either individually or in conjunction, generally achieved the best performance. We provide a description of the dataset and the task setup, including the evaluation settings, and we briefly overview the participating systems. As is customary in the CheckThat! lab, we have release all datasets from the lab as well as the evaluation scripts to the research community. This will enable further research on finding relevant check-worthy content that can assist various stakeholders such as fact-checkers, journalists, and policymakers.
Overview of JOKER 2023 Automatic Wordplay Analysis Task 1 - Pun Detection

This paper presents details of Task 1 of the JOKER-2023 Track, which aims to detect sentences in English, French, and Spanish that contain wordplay. With applications in humour generation, sentiment analysis, conversational agents, content filtering, and linguistic creativity, this task is still challenging despite significant recent progress in information retrieval and natural language processing. Building on the lessons learned from last year’s edition of the JOKER track, our overall goal is to foster progress in the automatic interpretation, generation, and translation of wordplay in English, Spanish, and French. In this paper, we define our task and describe our approaches to corpus creation and evaluation in the three languages. We then present an overview of the participating systems, including summaries of their approaches and a comparison of their performance.
Glaciological investigations at Nigardsbreen, Norway

One of the largest outlet glaciers from the ice cap Jostedalsbreen has been studied by various scientists during the 19th and 20th centuries, and a detailed programme of mass balance investigations has been carried out in recent years. The paper summarizes results from earlier studies, even from observations since the large advance about 1740, and particularly recent data. Detailed mass balance studies since 1962 have shown that the glacier is growing in volume (more than 6 m of water equivalent from 1962 to 1975) but this has not yet resulted in any advance of the ice front. The maximum observed ice velocity is 1.4 m/day on the tongue, where the ice thickness is about 200 m in a surveyed profile located below the ice falls.
Extended Overview of DocILE 2023: Document Information Localization and Extraction

This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets. This is an extended version of the condensed overview paper [1].
Open Web Search at LongEval 2023: Reciprocal Rank Fusion on Automatically Generated Query Variants

We describe the participation of the Open Web Search (OWS) team in the shared task LongEval hosted at CLEF 2023. Our submission is motivated by previous observations on static test collections that fusing search results for multiple user-generated query variants for the same information need improves the overall retrieval effectiveness. However, it is unclear whether these improvements are robust over time, or to what extent the query variant generation needs to be adjusted if queries and relevance assessments change. We evaluate the robustness of query variants generated by ChatGPT by optimizing the prompt and other retrieval hyperparameters using training data collected in June 2022, and evaluating the rank fusion effectiveness on test data from July and September 2022, respectively.
SEUPD@CLEF: Team FADERIC on A Query Expansion and Reranking Approach for the LongEval Task

This report explains and analyzes the system developed by Team FADERIC for the LongEval Lab at CLEF 2023, Task 1 - LongEval-Retrieval. The team members are all students following the Search Engines course a.y. 2022/23 at the Computer Engineering master degree at University of Padua. The system developed is a search engine that has to retrieve documents from a corpus, composed of original files in French language and automatically translated files in English language. The produced IR system exploits the query expansion technique, such as word N-grams and synonyms, and also the use of a reranking to improve the overall performance. Evaluating the longitudinal effectiveness of the system using the multiple collections provided by CLEF, we show that the performances remain satisfactory over time.
BEAVER-POND SEQUENCE MORPHOLOGY AND SEDIMENTATION IN NORTHWESTERN MONTANA

Two adjacent beaver-pond sequences of seven ponds each were examined along the eastern boundary of Glacier National Park, Montana, to determine spatial patterns and amounts of sedimentation, both within individual ponds and along pond sequences. Dams and their associated ponds were categorized into two age groups, young versus old, based on dam morphology and surface vegetation. Old ponds contained significantly more sediment than did young ponds. However, hypothesized patterns of downstream reductions in coarse-grained sediment, both within a pond and along a sequence, were not observed, nor were there any discernible patterns in sediment texture across ponds. [Key words: beaver pond, sedimentation, Rocky Mountains, Montana. ]
Allele-speciﬁc expression at the androgen receptor alpha gene in a hybrid unisexual ﬁsh, the Amazon molly ( Poecilia formosa )

The all-female Amazon molly ( Poecilia formosa ) is the result of a hybridization of the Atlantic molly ( P . mexicana ) and the sailfin molly ( P . latipinna ) approximately 120,000 years ago. As a gynogenetic species, P . formosa needs to copulate with heterospecific males including males from one of its bisexual ancestral species. However, the sperm only triggers embryogenesis of the diploid eggs. The genetic information of the sperm donor typically will not contribute to the next generation of P . formosa . Hence, P . formosa possesses generally one allele from each of its ancestral species at any genetic locus. This raises the question whether both ancestral alleles are equally expressed in P . formosa . Allele-specific expression (ASE) has been previously assessed in various organisms, e.g., human and fish, and ASE was found to be important in the context of phenotypic variability and disease. In this study, we utilized Real-Time PCR techniques to estimate ASE of the androgen receptor alpha ( ar α ) gene in several distinct tissues of Amazon mollies. We found an allelic bias favoring the maternal ancestor ( P . mexicana ) allele in ovarian tissue. This allelic bias was not observed in the gill or the brain tissue. Sequencing of the promoter regions of both alleles revealed an association between an Indel in a known CpG island and differential expression. Future studies may reveal whether our observed cis-regulatory divergence is caused by an ovary-specific trans-regulatory element, preferentially activating the allele of the maternal ancestor.
Journal Pre-proof Innovative improvement of sintered ceramic electrolytes by salt infiltration

Previously sintered (1500 °C, 4 h) dense pellets of Ce 0.9 Gd 0.1 O 1.95 (GDC) were covered and heat treated with eutectic mixtures of Na 2 CO 3 and Li 2 CO 3 (NLC), and their electrical performance was assessed against pure GDC and chemically synthesized GDC+NLC. Microstructural analysis of NLC impregnated samples confirmed slight migration of the molten phase to the interior of the GDC pellets via grain boundaries, resulting in a significant improvement of the grain boundary conductivity, increasing with duration of heat treatment (0.5-2 h) and temperature (600 to 800 ºC range). The observed total conductivity exceeded in almost 20% the corresponding values obtained for standard GDC samples. Cells tested before and after direct current polarization (0.5 V, 500 °C) showed the same electrical performance, discarding the possibility of parallel contributions of salt ions to the total conductivity. Grain boundary engineering using salt infiltration is an effective tool to improve the electrical performance of ceramic electrolytes.
Moral Disengagement Among Serious Juvenile Offenders: A Moral Disengagement Among Serious Juvenile Offenders: A Longitudinal Study of the Relations between Morally Disengaged Longitudinal Study of the Relations between Morally Disengaged Attitudes and Offending Attitudes and Offending

The present study investigates the relation between moral disengagement—one’s willingness to conditionally endorse transgressive behavior—and ongoing offending in a sample of adolescent male felony offenders ( N (cid:1) 1,169). In addition, the study attempts to rule out callous–unemotional traits as a third variable responsible for observed associations between moral disengagement and offending. A bivariate latent change score analysis suggests that reduction in moral disengagement helps to speed decline in self-reported antisocial behavior, even after adjusting for the potential confound of callous–unemotional traits. Declines in moral disengagement are also associated with declining likelihood of offending, based on official records. Given that both moral disengagement and offending tend to decrease over time, these findings suggest that changing attitudes toward antisocial behavior contribute to desistance from offending among delinquent youth.
Improved determination of d ( x ) − u ( x ) ﬂavor asymmetry in the proton by data from the BONuS experiment at JLAB and using an approach by Brodsky, Hoyer, Peterson, and Sakai

The experimental data taken from both Drell-Yan and deep-inelastic scattering experiments suggest a sign change in ¯ d ( x ) − ¯ u ( x ) ﬂavor asymmetry in the proton at large values of momentum fraction x . In this work, we present a phenomenological study of ¯ d ( x ) − ¯ u ( x ) ﬂavor asymmetry. First, we extract the ¯ d ( x ) − ¯ u ( x ) distribution using the more recent data from the BONuS experiment at Jefferson Lab on the ratio of neutron to proton structure functions, F n 2 /F p 2 , and show that it undergoes a sign change and becomes negative at large values of momentum fraction x , as expected. The stability and reliability of our obtained results are examined by including target mass corrections as well as higher twist terms which are particularly important in the large-x region at low Q 2 . Then, we calculate the ¯ d ( x ) − ¯ u ( x ) distribution using the Brodsky-Hoyer-Peterson-Sakai model and show that if one chooses a mass for the down quark smaller than the one for the up quark it leads to a better description for the Fermilab E866 data. To prove this claim, we determine the masses of down and up sea quarks by ﬁtting to the available and up-to-date experimental data for the ¯ d ( x ) − ¯ u ( x ) distribution. In this respect, unlike the previous theoretical studies, we have shown that this distribution has a sign change at x > 0 . 3 after evolution to the scale of available experimental data.
Inﬂation, Debt, and Default 1

We show that the co-movement of inﬂation and domestic consumption growth affects both the pricing and the dynamics of nominal domestic debt. In particular a positive co-movement of inﬂation and consumption makes returns on government bonds negatively correlated with domestic consumption: this lowers risk premia on nominal domestic debt as seen in the data. However, as this co-movement increases, the debt becomes more risky for the government and reduces its incentives to accumulate debt. We ﬁnd that debt accumulation is overall stronger the higher the co-movement of inﬂa-tion and consumption. To assess these joint equilibrium properties of debt and interest rates, we calibrate a simple model of domestic default and nominal debt in the presence of exogenous inﬂation risk and domestic risk averse agents. Consistent with the data, the model also reveals that increased co-movement of inﬂation and consumption leads to more volatile interest rates.
Modelling ammonium and nitrate uptake by a mature Douglas-fir stand from a soil with high atmospheric NH X input

Model calculations were performed on N uptake by 40-year-old Douglas-fir trees from a sandy soil situated in an area with a high NH X input level. Two model approaches were ap­ plied to distribute N uptake over the root system and over the two N sources, ammonium and nitrate. In the first approach, priority was given to maximizing the relative ammonium con­ tribution to N uptake; the other approach aimed at a uniform N uptake rate for all roots. To­ tal N uptake was the same with both approaches, but the distribution of N uptake over roots in various layers was different, as was also the contribution of ammonium to total-N uptake. During a large part of the growing season the amount of N that could be taken up by the roots was lower than the N demand of the trees. Ammonium accounted for at least one quarter and at most about half of the amount of N taken up by the trees, which uncertainty resulted from the different model approaches used and from variability in input data. Large differences in relative ammonium contribution to N uptake existed among roots in various soil layers. In the upper soil layers, the ammonium contribution was considerable during a large part of the growing season, so acidification of the rhizosphere may be expected. In deeper layers, main­ ly nitrate was taken up, so the rhizosphere will be alkalized. Possible effects of changes in NH X input level, nitrification rate and tree growth on N uptake are discussed.