Miracle-dz/UniFAR-FTUs huggingface dataset:
Information System for the Vegetation Control of Transmission Lines Right-of-way

Surveillance and monitoring of the right-of-ways in all the Brazilian transmission lines, regarding the clearing and cleaning, are periodically done through field inspection or by using a helicopter. Once identified any irregularity, the EATE Company begins its plan for the next cleaning and clearing task. One of the Company's line, located in the Northern part of Brazil (Amazon area), deserves special attention due to dense vegetation surrounding this line right-of-way. Information Systems containing geo-referenced data of the transmission assets, lands where the line passes, vegetation around the line and the applicatives utilized are useful tools to improve the efficiency of the maintenance engineering. Satellite images, aerophotogranometry, videography and airborne laser scanning are now available in the market. Due to the accuracy they offer (spatial resolution), these tools can well be applied to track not only invasions to the transmission line right-of-way but also to monitor the surrounding vegetation. Among the aspects considered within the scope of this paper are the following: -Development of a GIS (Geographic Information System) and its methodology aimed to the right-of-way surveillance using images, georeferenced database and a geoprocessing software. -Development of a transmission line 3D tracing using airborne laser scanning data.
Overview of the CLEF-2023 CheckThat! Lab Task 4 on Factuality of Reporting of News Media

We present an overview of the CLEF-2023 CheckThat! lab Task 4, which focused on predicting the factuality of reporting of entire news outlets. This is a different level of granularity compared to previous efforts, which focused on fact-checking, where the target is a claim, or fake news detection, where the target is an article. We briefly summarize the participating systems and discuss the dataset, the task, and the evaluation setup. The task attracted a large number of registrations, and eventually five teams made submissions. All participants improved over the baseline by a margin using both deep learning and traditional machine learning approaches. We make the dataset and the associate code freely available to the research community with the aim to promote further research on this problem.
Memory efficient location recommendation through proximity-aware representation

Sequential location recommendation plays a huge role in modern life, which can enhance user experience, bring more profit to businesses and assist in government administration. Although methods for location recommendation have evolved significantly thanks to the development of recommendation systems, there is still limited utilization of geographic information, along with the ongoing challenge of addressing data sparsity. In response, we introduce a Proximity-aware based region representation for Sequential Recommendation (PASR for short), built upon the Self-Attention Network architecture. We tackle the sparsity issue through a novel loss function employing importance sampling, which emphasizes informative negative samples during optimization. Moreover, PASR enhances the integration of geographic information by employing a self-attention-based geography encoder to the hierarchical grid and proximity grid at each GPS point. To further leverage geographic information, we utilize the proximity-aware negative samplers to enhance the quality of negative samples. We conducted evaluations using three real-world Location-Based Social Networking (LBSN) datasets, demonstrating that PASR surpasses state-of-the-art sequential location recommendation methods
A Multi-Feature Custom Classification Approach to Authorship Verification

PAN 2023 conducted Cross Discourse Authorship Verification task to evaluate the systems that identify if a pair of text was authored by the same person. This paper talks about our approach towards this task. We used multiple stylometric features such as character n-grams, POS tags, character and word count etc. to represent the texts. The predictions were done using a custom classifier which uses cosine similarities between the text of an author and the input pair of texts. This submission of ours achieved the AUC of 0.985 on the training data set.
Overview of ImageCLEFfusion 2023 Task - Testing Ensembling Methods in Diverse Scenarios

This paper presents a comprehensive overview of the second edition of the ImageCLEFfusion task, held in 2023. The primary goal of this endeavor is to facilitate the advancement of late fusion or ensembling methodologies, which possess the capability to leverage prediction outcomes derived from pre-computed inducers to generate superior and enhanced prediction outputs. The present iteration of this task encompasses three distinct challenges: the continuation of the previous year’s regression challenge utilizing media interestingness data, where performance is measured via the mAP at 10 metric; the continuation of the retrieval challenge involving image search result diversification data, where performance is measured via the F1-score and Cluster Recall at 20; and the addition of a new multi-label classification task focused on concepts detection in medical data, where performance is measured via the F1-score. Participants were provided with a predetermined set of pre-computed inducers and were strictly prohibited from incorporating external inducers during the competition. This ensured a fair and standardized playing field for all participants. A total of 23 runs were received and the analysis of the proposed methods shows diversity among them ranging from machine learning approaches that join the inducer predictions to ensemble schemes that learn the results of other methods.
Overview of the CLEF 2023 SimpleText Task 1: Passage Selection for a Simplified Summary

This paper presents an overview of the CLEF 2023 SimpleText Task 1: Content Selection, asking systems to retrieve scientific abstracts in response to a query prompted by a popular science article. Overall, the SimpleText track provides an evaluation platform for the automatic simplification of scientific texts. We discuss the details of the task set-up. First, the SimpleText Corpus with over 4 million academic papers and abstracts. Second, the Topics based on 40 popular science articles in the news and the 114 Queries prompted by them. Third, the Formats of requests and results, the Evaluation labels and Evaluation measures used. Fourth, the Results of the runs submitted by our participants.
ES-VRAI at CheckThat!-2023: Leveraging Bio and Lists Information for Enhanced Rumor Verification in Twitter

This paper presents our participation in the CLEF2023 CheckThat! Lab [1], focusing on Task 5, which addresses Authority Finding in Twitter [2]. This study addresses the gap in rumor verification by exploring the use of Bio and Lists information from trusted authorities’ bios as evidence sources in social media. A Twitter Bio is a brief description or introduction about oneself or an organization displayed on a user’s profile, providing a snapshot of their identity, interests, or purpose. Twitter Lists are curated groups of Twitter accounts created by users to organize and categorize specific individuals or topics for easy viewing and engagement. Previous research has primarily focused on propagation networks and user-generated content. The findings highlight the potential of incorporating Bio and Lists information to enhance existing rumor verification systems. Additionally, the study evaluates different retrieval approaches, showing that combining Bio and Lists information leads to effective lexical retrieval. A hybrid approach combining lexical and semantic retrieval further improves performance. These findings contribute to advancing rumor verification methods in social media.
University of Amsterdam at the CLEF 2023 SimpleText Track

This paper reports on the University of Amsterdam’s participation in the CLEF 2023 SimpleText track. Our overall goal is to investigate and remove barriers that prevent the general public from accessing scientific literature, hoping to promote science literacy among the general public. Our specific focus is to investigate the relation between the topical relevance and the text complexity of the retrieved information within the context of the track’s setup. Our results suggest that text complexity is an essential aspect to consider for improving non-expert access to scientific information, and opens up new routes to develop effective scientific information access technology tailored to needs of the general public.
THE MAINTENANCE OF FOREST TRUCK ROADS IN FOREST MANAGEMENT REGIONS OF SARAJEVO CANTON

UDK: 630*38:625.711. 84(497.6 Sarajevo) The time period of forests opening and construction of forest truck roads in the analyzed area is longer than half of century. In such long time interval different types of vehicles for transport of timber assortments have been applied. Due to this fact technical elements of constructed roads are different. The vehicles currently used for the transport of timber assortments cause an accelerated destruction of forest truck roads due to inappropriate carriage way quality. Besides traffic load the forest truck roads are exposed to permanent atmospheric influences. The above specified and other reasons condition obligatory and regular maintenance and reconstruction of forest truck roads depends on the current condition of forest truck roads as well as on the plans for further use. In this article the condition of forest truck roads in the Forest management regions of Sarajevo Canton has been determined. The most frequent types of damages and causes of their occurrence have been registered. The methods for maintenance and rehabilitation-reconstruction of forest truck roads have been selected and their costs have been calculated. In the time when construction of new kilometers of forest truck roads is more and more rare, timely, adequate and financially justified maintenance of the existing network of forest truck roads is one of the key question for their undisturbed use. On the basis of results obtained in this research the guidelines for the time period and the ways of maintenance of forest truck roads have been given. The application of these guidelines should help in achieving of the best and most economical total results through the long-term use of forest truck roads.
Leverage Samples with Single Positive Labels to Train CNN-based Models For Multi-label Plant Species Prediction

Understanding the geographical distribution of plant species is useful in many scenarios related to biodiversity management and conservation. By associating plant species occurrences with environmental features of each location, we can model the relationship between an environment and the species. However, the cost of multi-label plant species annotation for a large dataset is expensive and time consuming, so it may only be possible to obtain a single positive label for each location. This type of dataset is provided in the GeoLifeCLEF 2023 competition, where learning multi-labels from single positive labels is the main challenge. In this report, we present our proposed models that achieved the best performance in GeoLifeCLEF 2023. We proposed several CNN-based models and a training strategy for learning samples with single positive labels. We conducted experiments to show the effectiveness of our method compared to a simple baseline on the provided dataset.
SurVIndel2: improving local CNVs calling from next-generation sequencing using novel hidden information

Deletions and tandem duplications are two major classes of structural variations. They frequently occur in highly repetitive regions, where existing methods fail to detect most of them. We previously introduced SurVIndel, an algorithm that tackled this issue by employing novel statistical methods and had improved sensitivity in repetitive regions. However, its precision was low. Here, we introduce SurVIndel2, an algorithm that uses a novel type of evidence (which we called hidden split reads) and also borrows and adapts the statistical approach of its predecessor. By combining these two approaches, SurVIndel2 is both more sensitive and more precise than any other method we tested
Sexism Identification In Social Networks

Sexism is a pervasive issue in society, and it has found a new platform for expression and dissemination in the digital age through social networks. Online social networks provide an environment where individuals can freely express their thoughts and opinions, but unfortunately, this freedom is sometimes misused to propagate sexist content, perpetuating harmful stereotypes and discrimination. In this paper, we present a sexism identification model/system specifically designed for social networks. Specifically, we describe the model submitted for the shared task on “sEXism Identification in Social network (EXIST 2023)” at CLEF2023.The problem concentrates on sexism detection in two languages: English and Spanish. The challenge is a binary classification problem to discover the instances of sexism in the tweets. Overall, the outcome of this project will provide valuable insights into the prevalence and nature of sexism in social networks. It can empower social network administrators, policymakers, and users to take proactive measures to combat sexism, promote gender equality, and create a safer online environment for everyone.
AUEB NLP Group at ImageCLEFmedical Caption 2023

This article describes the methods that the AUEB NLP Group experimented with during its participation in the 7th edition of the ImageCLEFmedical Caption sub-tasks, namely Concept Detection and Caption Prediction. The former intends to automatically classify biomedical images into a set of one or more tags based solely on the visual input, while the latter aims to generate a syntactically and semantically accurate diagnostic caption that addresses the medical conditions depicted on a given image. For the Concept Detection sub-task, extending our previous work, we utilized a wide range of Convolutional Neural Network encoders followed by a Feed-Forward Neural Network, both in a single-task and a multi-task fashion, as well as combined with a contrastive learning approach. Our methods concerning the Caption Prediction sub-task are influenced by both our previous work and recent progress in Natural Language Processing (NLP) methods. Our two base systems use CNN-RNN and Transformer-to-Transformer encoder-decoder architectures, respectively. Additionally, we experimented with a Transformer-based denoising component, which was trained to reformulate the generated captions in a more syntactically coherent and medically accurate way. Our group ranked 1 st in Concept Detection and 3 rd in Caption Prediction.
Bird Species Recognition using Convolutional Neural Networks with Attention on Frequency Bands

This paper presents a deep learning approach for recognizing bird species in soundscape recordings using Convolutional Neural Networks (CNNs). The proposed method extends CNNs with a classification head that incorporates attention on frequency bands. The models are trained on a large dataset of bird sounds and employ various data augmentation techniques to improve performance and address the domain shift between training and test data. The effectiveness of the approach is evaluated in the BirdCLEF 2023 competition, hosted on Kaggle, where it achieves a macro-averaged mean average precision (cmAP) of 76.3 % on the official test set. This performance positions the method among the top 3 systems to accurately identify birds in wildlife monitoring recordings around Northern Mount Kenya.
AIMultimediaLab at ImageCLEFmedical GANs 2023: Determining "Fingerprints" of Training Data in Generated Synthetic Images

This paper presents the participation of the AI Multimedia Lab to the 2023 ImageCLEF medical GANs task. The 2023 ImageCLEFmedical GANs task challenges participants to examine the hypothesis that generative models (Generative Adversarial Networks — GAN) are generating medical images that contain “fingerprints” of the real images used for the training of the network. We present our team’s approach to tackle this task, consisting of a method that implies generating synthetic images from the development dataset. Subsequently, features were extracted from both sets of generated images (the one provided in the development dataset and the one we generated). A binary Support Vector Machine (SVM) classifier was trained using these features and the labels were predicted for the real images from the test dataset. Experimentation on the testing dataset show promising results.
Overview of the CLEF 2023 SimpleText Task 2: Difficult Concept Identification and Explanation

In this paper, we present an overview of the “Task 2: What is unclear? Difficult concept identification and explanation” within the context of the Automatic Simplification of Scientific Texts (SimpleText) lab, run as part of CLEF 2023. The primary objective of the SimpleText lab is to advance the accessibility of scientific information by facilitating automatic text simplification, thereby promoting a more inclusive approach to scientific knowledge dissemination. Task 2 focuses on complexity spotting within scientific texts (passage). Thus, the goal is to detect the terms/concepts that require specific background knowledge for understanding the passage, assess their complexity for non-experts, and provide explanations for these detected difficult concepts. A total of 39 submissions were received for this task, originating from 12 distinct teams. In this paper, we describe the data collection process, task configuration, and evaluation methodology employed. Additionally, we provide a brief summary of the various approaches adopted by the participating teams.
Overview of ImageCLEFmedical 2023 - Caption Prediction and Concept Detection

The ImageCLEFmedical 2023 Caption task on caption prediction and concept detection follows similar challenges held from 2017–2022. The goal is to extract Unified Medical Language System (UMLS) concept annotations and/or define captions from image data. Predictions are compared to original image captions. Images for both tasks are part of the Radiology Objects in COntext version 2 (ROCOv2) dataset. For concept detection, multi-label predictions are compared against UMLS terms extracted from the original captions with additional manually curated concepts via the F1-score. For caption prediction, the semantic similarity of the predictions to the original captions is evaluated using the BERTScore. The task attracted strong participation with 27 registered teams, 13 teams submitted 116 graded runs for the two subtasks. Participants mainly used multi-label classification systems for the concept detection subtask, the winning team AUEB-NLP-Group used an ensemble of three CNNs. For the caption prediction subtask, most teams used encoder-decoder architectures, with the winning team CSIRO using an encoder-decoder framework with an additional reinforcement learning optimization step.
Evidence Retrieval for Causal Questions Using Query Expansion And Reranking

We present our runs to the Touché Lab on Argument and Causal Retrieval at CLEF 2023, aiming to retrieve relevant documents for given causal questions. Our approaches rely heavily on query expansions. Both a transformer-based and a technique using expansions using prior knowledge on the stated relationships are used. The retrieved documents are then retrieved using the ChatNoir search engine before being subjected to a simple reranking scheme, ranking documents higher the earlier the causal relationship in question is mentioned.
StellEllaStars at MEDIQA-Sum 2023: Exploring Transformer-Based models for Dialogue2Topic Classification

Topic labeling of clinical notes is an essential task in Clinical Natural Language Processing. In this study, we explore deep neural network-based classification approaches to assign predetermined topic categories to conversation snippets between doctors and patients. Our proposed models 1 include transformer-based models like BERT, traditional machine learning models like Logistic Regression, and deep learning models such as Continuous Bag-of-Words (CBoW). To address the issue of the lack of sufficient data due to a small training dataset, we incorporate oversampling techniques into our methods. Our proposed approaches perform fairly well on the MEDIQA-Sum 2023 shared task on classifying doctor-patient dialogues into topic categories, with our best run which leverages the ClinicalBERT model achieving an accuracy score of 0.765 on the MEDIQA-Sum test set.
SEUPD@CLEF: Team GWCA on Longitudinal Evaluation of IR Systems by Using Query Expansion and Learning To Rank

This paper presents the results and work done by team GWCA (University of Padua) on LongEval CLEF 2023 Lab in Task 1 (retrieval), that aims at evaluating the temporal persistence of information retrieval systems. Afteracareful analysis on the dataset given to train the system, the group decided to first try some common techniques to improve performance and then focus on those that produced better experimental results.
BIT.UA at BioASQ 11B: Two-Stage IR with Synthetic Training and Zero-Shot Answer Generation

This paper presents the efforts of the Biomedical Informatics and Technologies (BIT) group at the University of Aveiro in the eleventh edition of the BioASQ challenge. This paper presents our efforts in the eleventh edition of the BioASQ challenge. We addressed Task B in its two phases: document retrieval (phase A) and question answering (phase B). In phase A, we utilized a sparse retrieval method for initial document retrieval, implemented using Anserini, followed by a re-ranking step using transformer models, including monoT5 and PubMedBERT. Phase B featured the application of large language models (LLMs) to generate answers to questions based on a relevant article, with models such as Alpaca-LoRA, OA-Pythia, and OA-LLaMA. We also explored a variety of prompts and question types, as well as different generation strategies to optimize our system’s performance. Our systems, in phase A, achieved competitive results scoring at the top and close to the top for all the batches, and achieving the best results in terms of F1 for all the batches. Regarding the phase B, our systems underperformed according to the automatic measures. Code to reproduce our submissions is available at https://github.com/ieeta-pt/BioASQ_11B.
Overview of the CLEF 2023 SimpleText Task 3: Simplification of Scientific Texts

This article provides a comprehensive summary of the CLEF 2023 SimpleText Task 3, which focuses on simplifying scientific text based on specific queries. The paper begins by explaining the motivation behind the task and providing an overview of the overall setup. It then proceeds to describe the test collection in detail, which includes a training set of sentences extracted from scientific abstracts along with corresponding simplified sentences created by human annotators. Additionally, a comprehensive test corpus of sentences is introduced, accompanied by meticulous annotations of lexical and syntactic complexity. The article concludes with an in-depth analysis, including information distortion and LLM hallucinations, of the simplified sentences submitted by participants and the resulting evaluation scores.