Miracle-dz/UniFAR-FTUs huggingface dataset:
Profiling Hate Speech Spreaders using Characters andWords N-grams

With the increase interactions in social networks, it is important to take care of the health of information and relationships between the users.One of the big problems today are hate speech within them, this type of comments as well as the users who share them can be very dangerous for the integrity of society. In this occasion we show a solution based on N-grams of characters and words for the task of "Profiling Hate Speech Spreaders on Twitter", as classifier we use SVM Support Vector Machines (libSVM) for English and Spanish corpus.
BERT-Based Embedding Model for Formula Retrieval

The web is a rich repository of mathematical information, the task of finding relevant information in such collection is a laborious one. However, numerous efforts have been made to develop web-accessible mathematical and scientific search systems in the recent past. In this paper, we have presented the BERT-based formula embedding model to facilitated formula retrieval in ARQMath2 tasks. To depict the performance of the pre-trained model for mathematical language processing tasks, the proposed BERT-based model is trained on the math exchange corpus of the ARQMath. It takes the L A TEX formulas as input and produced the fixed dimensional embeddings for the same. For similarity measure, the cosine similarity has been used. The obtained results have shown that the proposed approach provides better fits than existing embedding approaches and infers the meaningful semantic relationships between equations.
Improving web user interface element detection using Faster R-CNN

Several challenges may arise when designing new user interfaces (UIs), e.g., because of communication between designers and developers, to which the detection of UI elements can help. The ImageCLEF DrawnUI 2021 challenge builds on the detection of such elements in two contest tasks: a Screenshot task that contains the website screenshot images with lots of noisy data, and a Wireframe task for detecting UI elements from hand-drawn proposals. This paper describes a simple algorithm based on the edge detection to filter noisy data from the website screenshots, and machine learning method which scored the first place in both tasks while having 0.628 and 0.900 mAP at 0.5 IoU in the Screenshot and Wireframe tasks. This method is based on the Faster R-CNN with a Feature Pyramid Network (FPN) that uses selected aspect ratios of anchor boxes according to the occurrences from the datasets. Thecode is available at https://github.com/vyskocj/ImageCLEFdrawnUI2021
Overview of the CLEF-2021 CheckThat! Lab Task 1 on Check-Worthiness Estimation in Tweets and Political Debates

We present an overview of Task 1 of the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The task asks to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in five languages: Arabic, Bulgarian, English, Spanish, and Turkish. A total of 15 teams participated in this task and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and RoBERTa. Here, we describe the process of data collection and the task setup, including the evaluation measures, and we give a brief overview of the participating systems. We release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in check-worthiness estimation for tweets and political debates. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Post-processing BioBERT And Using Voting Methods for Biomedical Question Answering

There have been remarkable advances in the field of Biomedical Question Answering (QA) through the application of Transfer Learning to overcome the scarcity of domain-specific corpora. The fine-tuning of BioBERT on general purpose larger datasets prior to fine-tuning on a specific biomedical task has proven to significantly improve performance. There are, however, a lot of post-processing techniques to the outputs of fine-tuned models to be explored. In this paper we present our QA system, developed for the BioASQ 9th challenge - Task B, Phase B, developed by our team - LASIGE_ULISBOA. Using the outputs from the fine-tuning of BioBERT on both the Multi-Genre Natural Language Inference (MNLI) and the Stanford Question Answering Dataset (SQuAD) datasets. We compare different post processing strategies for prediction retrieval for Yes/No, Factoid, and List type questions. We show that using Softmax in the proper location of the pipeline of answer retrieval leads to better performance and also increases the explainability of a prediction’s confidence level in QA. We also present a method for applying voting system algorithms to choose candidates for List type answers, how they can increase MacroF1 score and how one can use them to optimize for either Precision or Recall. The obtained results, averaged over batches, were 0.798 MacroF1 for Yes/No, 0.478 MRR for Factoid, and 0.466 F1 for List. The used software is available in an open access repository.
SSN MLRG at VQA-MED 2021: An Approach for VQA to Solve Abnormality Related Queries using Improved Datasets

The Visual Question Answering (VQA) in the medical domain attains tremendous advancement in last few years. To improvise the VQA research, ImageCLEF forum is organizing the fourth edition of VQA task in medical domain. This year, the abnormality related VQA queries are to be answered for the given set of radiology images. In the proposed system, VGGNet based on transfer learning approach and LSTM is used to extract image and text features respectively. The extracted three dimensional (embedding, image, text) feature vectors are concatenated into sequence of vectors by LSTM for predicting the answer. The purpose of selecting VGGNet and LSTM are: VGGNet, outperforms complex recognition tasks and also addresses vanishing gradient and exploding gradient problem and LSTM, solves complex sequence learning problems and overcomes long term dependency problems. In addition, the hyper parameters are chosen appropriately and four improved datasets are used to analyze the performance of the proposed model. These four datasets are build by collecting the samples from previous ImageCLEF VQA – MED tasks. The proposed model resulted in an accuracy of 0.196 and a BLEU score of 0.227 for one of the dataset, which is ranked tenth among all participating groups in ImageCLEF 2021 VQA-MED task.
Permission and Privacy Challenges in Alternate-Tenant Smart Spaces

: We explore a ‘Smart-BnB scenario’ whereby someone (an Owner) advertises a smart property on a web platform. Renters use the platform for short periods, and may fully enjoy the property, including its smart features such as sensors. This scenario should further ensure the Renter’s privacy, so we use consent receipts and selective sharing. This paper describes a demonstrator of how smart environments can operate in a privacy respecting manner.
Predicting Sign of Depression via Using Frozen Pre-trained Models and Random Forest Classifier

Predicting and understanding how various mental health conditions present online in textual social media data has become an increasingly popular task. The main aim of using this type of data lies in utilising its findings to prevent future harm as well as to provide help. In this paper, we describe our approach and findings in participating in sub-task 3 of the CLEF e-risk shared task. Our approach is based on pre-trained models plus a standard machine learning algorithm. More specifically, we utilise the pre-trained models to extract features for all user’s posts and then feed them into a random forest classifier, achieving an average hit rate of 32.86%.
Team Sigmoid at CheckThat!2021 Task 3a: Multiclass fake news detection with Machine Learning

Fake news is affecting our lives since the internet has become popular. Particularly, in this era of social media it is very easy to spread and be affected by fake news. In this work we have developed machine learning models which can classify a news claim into four classes. This work has been done under the competition of CheckThat! 2021 task-3a. We have conducted our experiment on Check that lab’s dataset. Our work has been done only on linguistic features. We have experimented both with traditional Machine Learning algorithms and Deep Learning algorithms. LSTM outperformed other traditional machine learning algorithms and with Adam optimizer LSTM gave a f1-macro score of 26.07%.
The Closed List is an Obstacle Too

The baseline approach for optimal path finding in 4-connected grids is A* with Manhattan Distance. Nevertheless, a large number of enhancements were suggested over the years, usually requiring a preprocessing phase and/or additional memory to store smart lookup tables. In this paper we introduce an enhancement to A* (called BOXA*) on grids which does not need any preprocessing and only needs negligible additional memory. The main idea is to treat the closed-list as a dynamic obstacle. We maintain a list of rectangles which surround CLOSED nodes and calculate an admissible heuristic using the fact that an optimal path from a given node must go around these rectangles. We experimentally show the benefits of this approach on a variety of grid domains.
Overview of the ImageCLEFmed 2021 Concept & Caption Prediction Task

The 2021 ImageCLEF concept detection and caption prediction task follows similar challenges that were already run from 2017–2020. The objective is to extract UMLS-concept annotations and/or captions from the image data that are then compared against the original text captions of the images. The used images are clinically relevant radiology images and the describing captions were created by medical experts. In the caption prediction task, lexical similarity with the original image captions is evaluated with the BLEU-score. In the concept detection task, UMLS (Unified Medical Language System) terms are extracted from the original text captions and compared against the predicted concepts in a multi-label way. The F1-score was used to assess the performance. The 2021 task has been conducted in collaboration with the Visual Question Answering task and used the same images. The task attracted a strong participation with 25 registered teams. In the end 10 teams submitted 75 runs for the two sub tasks. Results show that there is a variety of used techniques that can lead to good prediction results for the two tasks. In comparison to earlier competitions, more modern deep learning architectures like EfficientNets and Transformer-based architectures for text or images were used.
HUKB at ChEMU 2021 Task 2: Anaphora Resolution

This paper describes our system for the ChEMU 2021 task 2 of anaphora resolution for extracting chemical reactions from patents. We divide the task into two subtasks: (1) span detection of mentions that include antecedent and anaphor; and (2) mention classification and relation detection. For the first task, we use a deep learning-based approach. For the second task, we use a rule-based approach that uses features related to chemical reactions by ChemicalTagger, which is a state-of-the-art text-mining tool for chemistry. Our system obtained an exact-match F-score of 0.6907 and a relaxed match F-score of 0.7459.
DPRL Systems in the CLEF 2021 ARQMath Lab: Sentence-BERT for Answer Retrieval, Learning-to-Rank for Formula Retrieval

This paper describes the participation of the Document and Pattern Recognition Lab from the Rochester Institute of Technology in the CLEF 2021 ARQMath lab. There are two tasks defined for ARQMath: (1) Question Answering, and (2) Formula Retrieval. Three systems were submitted for Task 1, all of which used two-stage retrieval models. First, a set of questions within the test collection that were similar to the query were found using a Sentence-BERT model that had first been trained on Quora Question Pairs and then fine tuned using duplicate question links found within the ARQMath test collection. Then in the second stage, answers given to those questions (identified using links within the collection) were ranked by one of three different similarity scores. For Task 2, five runs were submitted: one using only formula embedding, another using formula embedding followed by re-ranking based on tree-edit distance, the third run using Tangent-S, and the remaining two being alternative ways of reranking Tangent-S results using learning-to-rank techniques.
Early Detection of Online Hate Speech Spreaders with Learned User Representations

We developed and evaluated models for early detection of online hate speech spreaders. We addressed the problem as a social media author profiling task: given a small collection of tweets, the goal is to predict whether the author is likely to spread hate speech in the future (e.g. against women or immigrants). We investigated the impact of augmenting standard lexical representations with learned user-level representations from author-topic models and neural user embeddings. The evaluation was conducted on a dataset created for the social media author profiling shared task at PAN 2021. Our results show that: (i) learned user representations capture latent user aspects that correlate with the propensity to spread hate speech; and (ii) different user representations are complementary and can combined to improve hate speech detection.
Team Skeletor at Touché 2021: Argument Retrieval and Visualization for Controversial Questions

Arguments are a critical part of education and political discourse in society, especially since more and more information is available online. In order to access this information, argument retrieval is a necessary task. In this work, we leverage the existing techniques of BM25 and BERT-based passage embedding similarity and introduce a new information retrieval technique based on manifold approximation. Evaluation results on the Touché @ CLEF 2021 topics and relevance scores show that the manifold-based approximation helps discover higher-quality arguments. Furthermore, we use these retrieval methods to visualize argument progression for users watching debates. The visualization results show promising directions for future exploration.
Nkovachevich at CheckThat! 2021: BERT fine-tuning approach to fake news detection

The success of a text classification approach depends to a large extent on the data that it is trained on. The adaptation of a model with thousands of weights, such as BERT, usually requires large amount of data. CLEF 2021 CheckThat! Lab for fake news detection offers a challenging multi-class task with relatively small data set to train on. The experiments, which include BERT fine-tuning, together with a couple of simpler algorithms, produce average results, and hardly overcome model and data size limitations. Nevertheless, they show a potential to be successful if implemented properly.
NLM at BioASQ Synergy 2021: Deep Learning-based Methods for Biomedical Semantic Question Answering about COVID-19

The COVID-19 outbreak has heightened the need for systems that enable information seekers to search vast corpora of scientific articles to find answers to their natural language questions. This paper describes the participation of the U.S. National Library of Medicine (NLM) team in BioASQ Task Synergy on biomedical semantic question answering for COVID-19. In this work, we exploited the pre-trained Transformer models such as T5 and BART for document re-ranking, passage retrieval, and answer generation. Official results show that among the participating systems, our models achieve strong performance in document retrieval, passage retrieval, and the “ideal answer” generation task. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Buckypaper bioelectrodes: emerging materials for implantable and wearable biofuel cells

Carbon nanotubes (CNTs) have been widely exploited for the development of enzymatic biofuel cells with sufficient power densities in the μW to mW range for operating low-power bioelectronic devices from renewable substrates. Buckypaper is a randomly oriented self-supporting film of carbon nanotubes, resembling an electronic paper, with excellent prospects for the construction of high performance enzymatic electrodes for use in biofuel cells. Attractive properties of buckypaper materials include large specific surface areas, high electrical conductivity, flexibility, biocompatibility, scalable production and the ability for efficient electron transfer with enzymes. Buckypapers are ideal self-supporting frameworks for enzymes and guest molecules such as metals, polymers and redox molecules, permitting the development of a wide range of catalytic bioelectrode interfaces. This review summarizes recent developments and advances of buckypaper bioelectrodes as an emerging component for body-integrated energy harvesting biodevices.
QMUL-SDS at CheckThat! 2021: Enriching Pre-Trained Language Models for the Estimation of Check-Worthiness of Arabic Tweets

This paper describes our submission to the CheckThat! Lab at CLEF 2021, where we participated in Subtask 1A (check-worthy claim detection) in Arabic. We introduce our approach to estimate the checkworthiness of tweets as a ranking task. In our approach, we propose to fine-tune state-of-art transformer based models for Arabic such as AraBERTv0.2-base as well as to leverage additional training data from last year’s shared task (CheckThat! Lab 2020) along with the dataset provided this year. According to the official evaluation, our submission obtained a joint 4th position in the competition where seven other groups participated.
Exploring Argument Retrieval for Controversial Questions Using Retrieve and Re-rank Pipelines

This notebook documents Team Macbeth’s contribution to the CLEF 2021 shared task Touché: Argument Retrieval for Controversial Questions . Our approach consists of different configurations of a two-step retrieve and re-rank pipeline. We experimented with sparse and dense approaches for argument retrieval and trained query-document cross-encoders for argument re-ranking. Our findings suggest that a sparse retriever combined with a custom re-ranker performed the best out of all our approaches
Document retrieval task on controversial topic with Re-Ranking approach

This paper is the report of the work done for Argument Retrieval CLEF 2021 Touché Task 1 by Shanks team (based in Italy and precisely the members are University’s of Padua students). Argument Retrieval CLEF 2021 Touché Task 1 focuses on the problem of retrieving relevant arguments for a given controversial topic, from a focused crawl of online debate portals. After some tests Shanks group has decided to parse the input documents taking only the title, premises and conclusion of the arguments (as well as the stance necessary to understand the arguments’ author point of view). After the indexing part of the documents the work is concentrate on how the retrieving and the raking are done. After some tests, we discover that the better results are obtained using a WordNet [1] based query expansion approach and a re-ranking process with two different similarity functions. This report describes in details how the documents parsing work and how the indexing and searching part are developed. The unexpected update of the qrels file did not allow us to re-run all the tests. In the end, however, we also reported the results of the runs obtained from parameter tuning on the new qrels.
Transfer Learning for Automated Responses to the BDI Questionnaire

This paper describes the participation of the DUTH-ATHENA team of Democritus University of Thrace and Athena Research Center in the eRisk 2021 task, which focuses on measuring the level of depression based on Reddit users’ posts. We address this task using both feature-based and fine-tuning strategies for applying BERT-based representations. In the feature-based approaches, we examine the possibilities of a SBERT model based on RoBERTa, pre-trained on Natural Language Inference (NLI) data and fine-tuned on STSb dataset to leverage transfer learning to depression-level estimation, and we achieve promising results. One of our runs ranks first in Average Hit Rate (AHR), while the others rank among the best four in the other evaluation metrics. Also, for the fine-tuning approach, we propose two predictive models that are built upon RoBERTa, which provide directions for future optimizations.
Phase Transitions and Hygroscopic Growth of Mg(ClO4)2, NaClO4, and NaClO4·H2O: Implications for the Stability of Aqueous Water in Hyperarid Environments on Mars and on Earth

In general pure liquid water is not thermodynamically stable on Mars due to the extremely cold and dry environment. The presence in the soil of perchlorates, which could lower the freezing point of water and form aqueous solutions by taking up water vapor even under subsaturated conditions, has been proposed to explain the possible existence of liquid water on Mars and in some hyperarid environments on Earth. In this work, the phase transitions and hygroscopic growth of Mg(ClO4)2, NaClO4, and NaClO4·H2O were investigated between 278 and 303 K. In this temperature range, we found that anhydrous Mg(ClO4)2 was completely converted to Mg(ClO4)2·6H2O at a relative humidity (RH) as low as <1%. In contrast, anhydrous NaClO4 was stable at RH below 20%, and NaClO4·H2O was completely transformed to anhydrous NaClO4 at <1% RH; when RH was increased to 30%, anhydrous NaClO4 was transformed to NaClO4·H2O. We also found that the deliquescence relative humidity (DRH) of NaClO4·H2O decreased from ∼51.5% at 278 K to ∼43.5.
RockNER: Evaluating the Robustness of Named Entity Recognition with Natural Adversarial Attacks

To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple and effective method to generate natural adversarial examples via entity-/context-level attacks. We apply the proposed attacking method on OntoNotes dataset and create a new benchmark named OntoRock to evaluate the robustness of a variety of existing NER models in a systematic evaluation protocol. Our experiments and analysis reveal that even the best model has a signiﬁcant performance drop (92.4% → 58.5% in F1). We ﬁnd that these models tend to memorize entity patterns instead of reasoning from the context. Apart from that, we also study the effects of a few data augmentation methods to improve the robustness of NER models. 1