Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Irony and Stereotype Spreaders Based On Convolutional Neural Networks

In order to solve the task of Profiling Irony and Stereotype Spreaders on Twitter released by PAN 2022 organizers, this paper proposes a two-layer convolutional neural network based on deep learning. This task is essentially a binary classification task.Due to its strong feature extraction ability, the convolutional neural network has been very active in the classification tasks of professional competitions[1]. The proposed model achieves an accuracy of 0.8 and a loss of 0.3 on the training set. The running time of the model is only 2 minutes.
SEUPD@CLEF: Team hextech on Argument Retrieval for Comparative Questions. The importance of adjectives in documents quality evaluation

This report explains our approach to solve the Task 2 challenge about Argument Retrieval for Comparative Questions proposed by the third Touché lab on argument retrieval at CLEF 2022. Given a comparative topic, the task is to retrieve relevant argumentative passages from a collection of documents for either compared object or for both. Our approach follows the Information Retrieval pipeline that is: the parsing of the document collection, the indexing of the parsed documents by using an analyzer with commons filters, and the query matching using a retrieval model. In addition, we implemented an index field that catches the overall quality of each passage for our specific task. From an analysis of our results, the implementation of the quality field slightly improves the ranking of the retrieved documents.
Classification of Plant Species Using AlexNet Architecture

Plant classification is the process of identifying plant species using image processing techniques. This system is helpful in identifying the different plant species which has useful applications in botanical studies and many plant based industries. The previous works have built systems based on data sets containing limited number of classes . However, the plant classification systems can further be improved in order to cover more number of species. The proposed system is capable of identifying 80000 classes of plant species and is built with a large dataset using AlexNet deep learning architecture. A combination of AdaGrad and KL Divergence optimization and loss functions respectively is used to train the model that produced a MRR score of 0.00029.
HPI-DHC @ BioASQ DisTEMIST: Spanish Biomedical Entity Linking with Pre-trained Transformers and Cross-lingual Candidate Retrieval

Biomedical named entity recognition and entity linking are important building blocks for various clinical applications and downstream NLP tasks. In the clinical domain, language resources for developing entity linking solutions are scarce: only a few datasets have been annotated on the level of concepts and the majority of concept aliases in target ontologies are only available in English. In such a resource-constrained setting, pre-training and cross-lingual transfer are promising approaches to improve performance of entity linking systems. In this paper, we describe our contribution to the BioASQ DisTEMIST shared task. The goal of the task is to extract disease mentions from Spanish clinical case reports and map them to concepts in SNOMED CT. Our system comprises a Transformer-based named entity recognition model, a hybrid candidate generation approach, and a rule-based reranking step. For candidate generation, we employ an ensemble of 1) a TF-IDF vectorizer based on character n-grams and 2) a cross-lingual SapBERT model. Our best run for the entity linking subtrack achieves a micro-averaged F 1 score of 0.566, which is the best score across all submissions in this track. A detailed analysis of system performance highlights the importance of task-specific entity ranking and the benefits of cross-lingual candidate retrieval.
AIMultimediaLab at ImageCLEFfusion 2022: DeepFusion Methods for Ensembling in Diverse Scenarios

The ImageCLEFfusion task addresses the challenging task of creating ensembling schemes and algorithms for fusing a large set of inducers in two particular scenarios: a regression scenario where the ground truth data consists of media interestingness annotations and a search result diversification scenario. We present our team’s approach for these two scenarios, consisting of a simple statistical baseline followed by the use of DeepFusion architectures and the way these architectures must be adapted for each scenario. The DeepFusion methods tested for media interestingness and result diversification are represented by deep neural networks consisting of dense, attention, convolutional, and Cross-Space-Fusion layers.
UNSL at eRisk 2022: Decision policies with history for early classification

For the 2022 edition of the CLEF eRisk Laboratory, our research group at Universidad Nacional de San Luis (UNSL) added new approaches and improvements concerning our last participation. We proposed two decision policies for EarlyModel that take into account historic information available to the models, and incorporated two score normalization steps into the SS3 model. We also significantly reduced the runtime to process the inputs. Despite not having achieved the best performances, our team obtained the best results for the ERDE 50 in tasks T1 and T2. Besides, considering the 𝐹 latency , we were the third-best team for both tasks. Finally, a couple of our models got some of the best performance for the ranking-based metrics for task T1.
Overview of the ImageCLEF 2022 Aware Task

The paper presents the overview of the ImageCLEF 2022 Aware task whose final objective is to make users more aware about the consequences of posting information on social networks. This is important insofar as users are often unaware about the effects of personal data sharing. Focus is put on modeling the impact of sharing impactful real-life situations such as searching for a bank loan, an accommodation, or a job. Since photos are one of the main types of data shared online, the task is instantiated as a photographic user profile assessment. Participants receive a training and validation dataset which includes a set of photographic profiles which are manually rated for each situation. They are required to train algorithms which rate and then rank test profiles in each tested situation. The correlation between automatic and manual profile rankings is used to measure the performance of algorithms. The overview discusses the task settings, the dataset constitution process, and the approaches proposed this year.
Profiling Irony and Stereotype Spreaders on Twitter Using TF-IDF and Neural Network

In this paper, we describe our participation in the author profiling task at PAN 2022.This task is mainly to profile the irony and stereotype spreaders on Twitter (IROSTEREO). We regard this task as a binary classification problem.Our proposed methods adopt TF-IDF, BiGRU and Text CNN models to extract the word frequency statistical features and deep semantic features of text respectively. Based on this series of features, the fully connected network layer is used to complete the classification prediction.Our final submitted system has an accuracy of 93.33% in the test set.This result verified the idea that word frequency statistical features and deep semantic features obtained by neural network jointly predict irony recognition.
Semi-supervised Multi-Label Classification with 3D CBAM Resnet for Tuberculosis Cavern Report

Detection and characterization of tuberculosis and the evaluation of lesion characteristics are challenging. To provide a solution for a multi-label classification task of tuberculosis cavern report task, we performed a deep learning study with backbones of 3D Resnet. Semi-supervised learning strategy was implied in this study to leverage the unlabeled dataset from cavern detection task. A convolutional block attention model (CBAM) was used to add an attention mechanism in each block of the Resnet to further improve the performance of the convolutional neural network (CNN). Our solution is ranked the 1st place with submissions obtained Mean_AUC of 0.687 and 0.681 for this task.
Opportunistic direct interconnection and cooperation between co-located wireless sensor networks

FACULTY OF PHYSICAL SCIENCES AND ENGINEERING SCHOOL OF ELECTRONICS AND COMPUTER SCIENCE Doctor of Philosophy Opportunistic Direct Interconnection and Cooperation Between Co-Located Wireless Sensor Networks by Teng Jiang As more Wireless Sensor Networks (WSNs) are deployed, cooperation between co-located networks becomes an increasingly important research topic. Existing WSN cooperation is based on traditional in-direct interconnection and data sharing has to rely on a backbone network. This is not flexible for co-located WSNs. In this thesis, the argument for a paradigm shift towards ‘Opportunistic Direct Interconnection’ (ODI) is made. Direct interconnection is referred to as the ability of a WSN to share information and resources with neighbouring networks without using additional facilities. As neighbouring networks will appear and disappear throughout the life of the network, such interconnection must be opportunistic as it cannot be conceived at design time. The first contribution of this research is the proposal of a framework towards achieving ODI. To demonstrate the feasibility of this framework, OI-MAC (Opportunistic Interconnection MAC) based solutions are developed. ODI schemes for different layers are designed and implemented as part of the protocols. With these ODI schemes, both networking-level and application-level interconnection among co-located WSNs can be established. The second contribution of this research is an in-depth analysis of ODI’s effects and performance. Using simulation, the effect on energy, nodal operation and packet delivery of interconnected WSNs is quantified. Also, the relationship between configuration and interconnection performance is figured out. The third contribution of this research is the development of cooperation case studies. These case studies reveal the potential benefits and performance of ODI-based cooperation. Results show that the cooperation among co-located WSNs cannot only bring functional benefits to the applications, but also economic benefits to the network stakeholders. The effect on the interconnected WSNs is also quantified and analysed. However, the side effects are believed to be outweighed by the potential applications and economic benefits.
Style Change Detection based on Prompt

The Style Change Detection 2022 aims to identify text positions within a given multi-author document at which the author switches. The previous method for detecting the style change used the Pretrained Language Model BERT to extract the interaction features of paragraphs as author style change. However, the models like BERT have a problem of inconsistent train and test objectives as there is no Mask Language Model (MLM) task when fine-tuning the downstream. Moreover, the effectiveness of features certainly lacks interpretability. For the above two points, this paper proposes a method of Style Change Detection based on Prompt (SCDP), a novel way of interacting with building a manual Prompt to reflect the writing style between two adjacent paragraphs or sentences by utilizing a pre-trained MLM. Using the proposed SCDP, we can settle the problem of inconsistent train and test objectives and increase the interpretability of the model, revealing the possibility of using a Pretrained Language Model-based Prompt for Style Change Detection.
Measuring the Severity of the Signs of Eating Disorders Using Similarity-Based Models

Measuring the severity of the signs of eating disorders is a new task introduced in the eRisk 2022 competition. The goal of this task is to infer the severity of the signs of Eating Disorders (ED) in social media users given their writings on said platform. These signs are inventoried by a standard self-report questionnaire. We presented two unsupervised similarity-based models using per-question feature sets that were developed using expert knowledge of discussions of ED online. These features are supported by dense word representations, namely GloVe and word2vec. The obtained results were modest as no training data were available.
CS_Morgan at ImageCLEFmedical 2022 Caption Task: Deep Learning Based Multi-Label Classification and Transformers for Concept Detection & Caption Prediction

This paper describes the participation of Morgan_CS in both Concept Detection and Caption Prediction tasks under the ImageCLEFmedical 2022 Caption task. The task required participants to automatically identifying the presence and location of relevant concepts and composing coherent captions for the entirety of an image in a large corpus which is a subset of the extended Radiology Objects in COntext (ROCO) dataset. Our implementation is motivated by using encoder-decoder based sequence-to-sequence model for caption and concept generation using both pre-trained Text and Vision Transformers (ViTs). In addition, the Concept Detection task is also considered as a multi concept labels classification problem where several deep learning architectures with “sigmoid” activation are used to enable multi-label classification with Keras. We have successfully submitted eight runs for the Concept Detection task and four runs for the Caption Prediction task. For the Concept Detection Task, our best model achieved an F1 score of 0.3519 and for the Caption Prediction Task, our best model achieved a BLEU Score of 0.2549 while using a fusion of Transformers.
Overview of the CLEF 2022 SimpleText Task 1: Passage Selection for a Simplified Summary

This paper presents an overview of the CLEF 2022 SimpleText track’s Task 1, asking systems to retrieve scientific abstracts in response to a query prompted by a popular science article. 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.
Ensembled Approach for Web Search Result Diversification Using Neural Networks

Result diversification provides a broader view of a topic, while maximizing the chances of retrieving relevant information. It avoids the bias in results, thus improving the user experience. This area finds a lot of applications in web searches and recommendation systems. The existing literature on this domain has achieved good accuracy on smaller datasets and using single models. An ensemble approach, using three neural network models, has been proposed to improve the existing predictions using a bigger dataset.
Overview of ImageCLEFfusion 2022 Task - Ensembling Methods for Media Interestingness Prediction and Result Diversification

The 2022 ImageCLEFfusion task is the first edition of this task, targeting the creation of late fusion or ensembling methods in two different scenarios: (i) the prediction of media visual interestingness, and (ii) social media image search results diversification. The objective proposed to participants is to train and test their proposed fusion schemes on a set of pre-computed inducers, without creating or bringing inducers from the outside. The two scenarios correspond to a regression scenario in the case of media interestingness, where performance is measured via the mean average precision at 10 (MAP@10) metric, and to a retrieval scenario in the case of result diversification, where performance is measured via the F1-score and Cluster Recall at 20 (F1@20, CR@20). Overall 6 teams registered for ImageCLEFfusion, 5 of them submitting runs, while only one team submitted runs to both the interestingness and diversification tasks. A total of 39 runs were received, and an analysis of the proposed methods shows a great diversity among them, ranging from statistical weighted approaches, weighted approaches that use learning stages for creating the weights, machine learning approaches that join the inducer predictions like SVM or KNN, deep learning approaches, and even fusion schemes that join the results of other fusion schemes.
NLP-IISERB@eRisk2022: Exploring the Potential of Bag of Words, Document Embeddings and Transformer Based Framework for Early Prediction of Eating Disorder, Depression and Pathological Gambling Over Social Media

The eRisk lab at CLEF 2022 had released three different tasks based on the posts of different users over Reddit, a popular social media. The first task was early detection of signs of pathological gambling. The second task was the early prediction of depression. The third one was assessing the severity of eating disorders over social media posts. The BioNLP research group at the Indian Institute of Science Education and Research Bhopal (IISERB) participated in all three tasks and submitted five runs using five different text mining frameworks for task 1 and task 2 and four different runs for task 3. The methods involve different feature engineering schemes and text classification techniques. The performance of the classical bag of words model, paragraph embedding technique and transformer-based models were explored to identify significant features from the given corpora. Moreover, we have identified features based on the biomedical concepts for pathological gambling using Unified Medical Language Systems, a repository for biomedical vocabularies. Subsequently, we have explored the performance of different classifiers, e.g., logistic regression, random forest etc. using various such features generated from the given data. The official results on the test data of individual tasks show that the proposed frameworks achieve top scores in terms of some of the evaluation techniques, e.g., precision, F1 score, speed etc. for all three tasks. The paper describes the performance, value and validity of the proposed frameworks for individual tasks and the scopes for further improvement.
Extended Overview of HIPE-2022: Named Entity Recognition and Linking in Multilingual Historical Documents

This paper presents an overview of the second edition of HIPE (Identifying Historical People, Places and other Entities), a shared task on named entity recognition and linking in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, HIPE-2022 confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. This shared task is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of HIPE-2022, run as an evaluation lab of the CLEF 2022 conference, is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets. Tasks, corpora, and results of participating teams are presented. Compared to the condensed overview [1], this paper contains more refined statistics on the datasets, a break down of the results per type of entity, and a discussion of the ‘challenges’ proposed in the shared task.
Exploiting Affective-based Information for Profiling Ironic Users on Twitter

This paper describes our approach to addressing the Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO) 2022 shared task. For classifying the users, we consider the number of ironic tweets they posted. The ironic content in a given tweet is determined according to an irony detection model which mainly relies on affective information. Different traditional classifiers were evaluated being the Random Forest the one with the highest performance. According to the official results, our system obtained a 0.933 accuracy rate. Additionally, a sub-task on stance detection focused in ironic users was also organized. For participating in this sub-task, we evaluated the same set of features than in the main task, obtaining the first place in the official ranking.
Similar but Different: Simple Re-ranking Approaches for Argument Retrieval

This work examines simple re-ranking approaches using the preprocessed args.me corpus to contribute to the Touché 2022 Argument Retrieval Task. The proposed retrieval system relies on an initial retrieval using a semantic search on a sentence level and takes advantage of simple heuristics. Our re-ranking approaches incorporate maximal marginal relevance , word mover’s distance , and a novel approach based on a fuzzy matching on part of speech tags that we call structural distance . Further, we explore the applicability of a graph-based re-ranking approach. The results show that the proposed re-ranking approaches could beat our baseline. For relevance, our re-ranking using structural distance performs best, while for quality, the one using the word mover’s distance achieves the highest score.
ZHAW at eRisk 2022: Predicting Signs of Pathological Gambling - GloVe for Snowy Days

This paper describes the participation of our team from ZHAW in the eRisk 2022 shared task. eRisk provides three challenges in the field of "Early Risk Prediction on the Internet". These are text classification problems to detect mental disorders. This paper focuses on addressing the first task whose main objective is the early detection of users at risk of pathological gambling, based on their Reddit history. We addressed this issue by developing models with GloVe as feature extraction and SVM and XGBoost as Classification. It has been found that XGBoost without dart boost has slightly better decision-based evaluation results than SVM.
Overview of the CLEF-2022 CheckThat! Lab Task 1 on Identifying Relevant Claims in Tweets

We present an overview of CheckThat! lab 2022 Task 1, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). Task 1 asked to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in six languages: Arabic, Bulgarian, Dutch, English, Spanish, and Turkish. A total of 19 teams participated and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and GPT-3. Across the four subtasks, approaches that targetted multiple languages (be it individually or in conjunction, in general obtained the best performance. We describe the dataset and the task setup, including the evaluation settings, and we give a brief overview of the participating systems. As usual in the CheckThat! lab, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research on finding relevant tweets that can help different stakeholders such as fact-checkers, journalists, and policymakers. © 2022 Copyright for this paper by its authors.
Authorship Verification Using Convolutional Neural Network

The PAN@CLEF 2022 Authorship Verification [8] is the task of determining if two texts are written by the same author based on comparing the texts’ writing styles. In this work, we propose a model that blends Bert and TextCNN [6] features and raises a data reorganization method to increase model training data to improve performance.
Isolated Profile Style Representation

This work shows the obtained results by the UO-UDC team at Profiling Irony and Stereotype Spreaders on Twitter shared task hosted by PAN22. We presented a hybrid model from BERT-like embeddings and the lexical representation of tweets. We exposed as experimental results the interactions and impact between combinations of representations in the final accuracy score. It is shown that it is not enough to represent a profile considering only independent features along with its corresponding class.