Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Document Expansion for Argument Retrieval

Processing of opinion based information is an increasingly relevant task in modern times. Especially in regards to complex and morally ambiguous topics, users search for high-quality information which leads to the necessity of automatically processing information of argumentative nature. This notebook documents our attempt to improve argument retrieval using expansion methods for documents as a contribution to Touché@CLEF 2021 as team Hua Mulan. Before runtime we expand arguments by predicting queries and hallucinating arguments using Transformer architectures and a more computational efficient approach based on TF-IDF. Compared to ad-hoc retrieval of the original args.me corpus with Dirichlet Language Model argument hallucination improved the baseline when evaluated on argument quality, while no improvements were obtained when argument relevance was evaluated.
Bird-Species Audio Identification, Ensembling 1D + 2D Signals

In this paper, a method for recognizing bird species in audio recordings is described. we have experimented with 4 different approaches. Model on Spectrograms and Waveform domain consists of two main models: 1) A binary classifier for predicting if bird call is present in the audio or not; 2) A multiclass classifier for predicting which bird is present. Combining these two approaches, 1D and 2D signals, gives strong results. We also experiment on ATDemucs which extends Demucs , replacing the BiLSTM with self-attention. In this approach, we first do source separation of multiple birds along with noise separation as Universal Source Separation. Then we classify each source, both using a 1D waveform model ReSE-Multi, with self-attention and a 2D spectrogram model. We also discuss how we handle different thresholds for different models by a postprocessing technique. Ensembling techniques like Voting, Scaling and Direct Averaging gave us a good boost in our results. Our combined architecture including 1D and 2D signals achieves 0.6179 micro-averaged F1 in the task that asked for classification of 397 bird species.
Profiling Hate Speech Spreaders on Twitter Task at PAN 2021

This overview presents the Author Profiling shared task at PAN 2021. The focus of this year’s task is on determining whether or not the author of a Twitter feed is keen to spread hate speech. The main aim is to show the feasibility of automatically identifying potential hate speech spreaders on Twitter. For this purpose a corpus with Twitter data has been provided, covering the English and Spanish languages. Altogether, the approaches of 66 participants have been evaluated.
IMS-UNIPD @ CLEF eHealth Task 2: Reciprocal Ranking Fusion in CHS

In this paper, we describe the results of the participation of the Information Management Systems (IMS) group at CLEF eHealth 2021 Task 2, Consumer Health Search Task. We participated in the three subtasks: Ad-hoc IR, Weakly Supervised IR, Document credibility. The goal of our work was to evaluate the reciprocal ranking fusion approach over 1) manual query variants; 2) different retrieval functions; 3) w/out pseudo-relevance feedback; 4) reciprocal ranking fusion.
Dowsing for Answers to Math Questions: Ongoing Viability of Traditional MathIR

We present our application of the math-aware search engine Tangent-L to the 2021 ARQMath Lab. This is a continuation of our MathDowsers submissions to last year’s Lab, where we produced the best Task 1 participant run. Since then, we have improved the search engine’s formula retrieval power by considering additional math features in the ranking function. This year, we also explore two approaches to incorporate proximity in evaluating the suitability of a document to be considered a match to a query. For the 2021 ARQMath Lab, our primary run in Task 1 produces an nDCG ′ value of 0.434, which is nearly five points higher than that produced by the second-best participant run. An unsubmitted run, which corrects the setup of the primary run and preserves duplicate keyword terms during query term extraction, produces an even higher nDCG ′ of 0.462. Meanwhile, our primary run in Task 2 produces an nDCG ′ value of 0.552, which is the best automatic run and is comparable to the best participant run, a manual run from the Approach0 team. The success of our runs continue to demonstrate that a traditional math information retrieval system remains a viable option for Community Question Answering specialized in the mathematical domain and for in-context formula retrieval.
Human Resource Management of Internet Enterprises Based on Big Data Mobile Information System

With the progress of science and technology, the advent of VR panorama, the era of Big Data has arrived, which has set off a revolution in the development and future destiny of Internet companies. Human resource management (HRM) is an important department of Internet companies. This paper introduces the principle and value of Big Data and related theories of HRM. This paper discusses three aspects of HRM in our company using Big Data theory: employee turnover prediction, salary comparison, and talent evaluation method, and puts forward suggestions for optimization and improvement of the company’s HRM. The results show that, through the company’s project manager’s ability to quantify data and analyze a set of data quantitative research, comparative analysis of the ability of three candidates Q, W, and E showed that W is the most suitable for the company’s project manager staff.
Profiling Hate Speech Spreaders on Twitter using stylistic features and word embeddings

This paper presents the different solutions proposed for the Profiling Hate Speech Spreaders on Twitter task at PAN 2021, which consists of classifying each author as hater or no hater from a set of tweets, for Spanish and English languages. The given approaches are different for each language. For Spanish, an ensemble of LSTM and a Logistic Regression model trained with stylistic features is used. For English, an ensemble of SVC and Random Forest model, also with stylistic features, is proposed. Our solutions achieved an accuracy of 83% in Spanish and 58% in English, resulting in an overall accuracy of 70.5% in the task ranking.
LIA@SimpleText2021: automatic query extraction from press outlets for mining related readable passages in scientific literature

We have developed a theoretical and software tool to assist science journalists in writing popular science and technology news articles. The public’s appropriation of the scientific debate and its consequences on political communication, which is often polarized or partisan, has led us to experiment with multi-nomial modeling in order to be able to automatically extract the associations of terms characteristic of a newspaper article with general circulation on a given period. To effectively compute these associations we compute the latent Dirichlet distributions (LDA) to a set of multinomials. We use recent implementations available in R and nonparametric correlation tests to verify the significance of associations automatically extracted by the model or formulated as hypotheses by the researcher. The detection of the vocabulary and systematic associations of a political communication is necessary for the study of the possible propagation of the ideas induced in the media, classic (such as the written press), encyclopedic (such as WikiPedia) or specialized ( such as DBLP).
Thor at Touché 2021: Argument Retrieval for Comparative Questions

We report on our approach for the Shared Task 2 from the second Touché lab [1] on argument retrieval at CLEF 2021. We retrieved and ranked the documents from ClueWeb12 to answer comparative questions using Okapi-BM25. We supplied the ranking model with 2 different kinds of expansion—query expansion with synonyms from WordNet and index expansion with arguments from Targer. Examination of different combination of expansions and search fields showed, that although the expansions benefit only one field while cause worse results for another, in case of synonyms it is still more beneficial to use both affected fields together with the expansion. We further re-rank the initial set of document candidates using elasticsearch and it’s built-in BM-25 algorithm.
Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.
Kdelab at ImageCLEF 2021: Medical Caption Prediction with Effective Data Pre-processing and Deep Learning

ImageCLEF 2021 Caption Prediction Task is an example of a challenging research problem in the field of image captioning. The goal of this research is to automatically generate accurate captions describing a given medical image. We describe our approach to captioning medical images and illustrate the text and image pre-processing that is effective for our task dataset. In this paper, we have applied sentence-ending period removal as text pre-processing and histogram normalization of luminance as image pre-processing. Furthermore, we present the effectiveness of our text data augmentation approach. Submission of our kdelab team on the task test dataset achieved a BLEU evaluating of 0.362.
Encoding Text Information By Pre-trained Model For Authorship Verification

Authorship verification is the task of deciding whether two texts have been written by the same author based on comparing the texts’ writing styles. We present a classification method based on encoding text information by a pre-trained model for authorship verification. The proposed model achieved the highest c@1 and F1-score on the small dataset of PAN Authorship Verification datasets.
Creation and Utilisation of Domain Specific Knowledge Graphs (DSKG) for E-Learning

: Educational domain models are building blocks for educational software. Unfortunately, such domain models require high manual effort to be created, are quickly outdated and are thus rarely used at all. A novel way to create such models is proposed with this paper, utilising knowledge mapping algorithms, natural language processing tools, a semantic web architecture and integration with online knowledge bases. The developed processes and tools make it fast and easy to create domain specific knowledge graphs (DSKG) automatically. Apart from defining DSKGs, two use-cases are presented that show how third-party tools may utilise DSKGs: (1) improved exam generation with EAs.LiT and (2) an assessment item and educational material recommender.
SCUoL at CheckThat! 2021: An AraBERT Model for Check-Worthiness of Arabic Tweets

Many people nowadays tend to explore social media to obtain news and find information about various events and activities. However, an abundance of misleading and false information is spreading every day for many purposes, dramatically impacting societies. Therefore, it is vitally important to identify false information on social media to help individuals distinguish the truth and protect communities from the harmful effects of false information. For this reason, determining which information has the priority to be scrutinized is a significant prior step that several studies have considered. In this paper, we have addressed Subtask-1A(Arabic) of CLEF2021 CheckThat! Lab. We have done that in two steps. The first involved pre-processing the provided dataset with text segmentation and tokenization. In the second step, we implemented different models on the Arabic tweets in order to binary classify them according to whether a specific tweet is worth being considered for fact-checking or not. We mainly compared two versions of the pre-trained AraBERT model with some of the traditional word encoding methods, including the Linear SVC model with TF-IDF. The results indicate that the AraBERTv2 version outperforms the other models. Consequently, we used it for our final submission, and we were ranked third among eight other participating teams.
Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure with Gated Recurrent Unit

Power quality disturbance (PQD) is essential for devices consuming electricity and meeting today’s energy trends. This study contains an effective artificial intelligence (AI) framework for analyzing single or composite defects in power quality. A convolutional neural network (CNN) architecture, which has an output powered by a gated recurrent unit (GRU), is designed for this purpose. The proposed framework first obtains a matrix using a short-time Fourier transform (STFT) of PQD signals. This matrix contains the representation of the signal in the time and frequency domains, suitable for CNN input. Features are automatically extracted from these matrices using the proposed CNN architecture without preprocessing. These features are classified using the GRU. The performance of the proposed framework is tested using a dataset containing a total of seven single and composite defects. The amount of noise in these examples varies between 20 and 50 dB. The performance of the proposed method is higher than current state-of-the-art methods. The proposed method obtained 98.44% ACC, 98.45% SEN, 99.74% SPE, 98.45% PRE, 98.45% F1-score, 98.19% MCC, and 93.64% kappa metric. A novel power quality disturbance (PQD) system has been proposed, and its application has been represented in our study. The proposed system could be used in the industry and factory.
UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims

Identifying check-worthy claims is often the first step of automated fact-checking systems. Tackling this task in a multilingual setting has been understudied. Encoding inputs with multilingual text representations could be one approach to solve the multilingual check-worthiness detection. However, this approach could suffer if cultural bias exists within the communities on determining what is check-worthy.In this paper, we propose a language identification task as an auxiliary task to mitigate unintended bias.With this purpose, we experiment joint training by using the datasets from CLEF-2021 CheckThat! , that contain tweets in English, Arabic, Bulgarian, Spanish and Turkish. Our results show that joint training of language identification and check-worthy claim detection tasks can provide performance gains for some of the selected languages.
Development of an IR System for Argument Search

Search engines are the easiest way to find the information that we need in our daily life, and they have became more and more powerful in the last years. Anyway, they are still far from perfection, and some problems afflict also the more advanced search engines. In this paper we discuss our approach to the problem of argument retrieval documenting our participation to the CLEF 2021 Touché Task 1. In particular, we present our IR system for the args.me corpus, a collection of documents extracted from web debate portals. After a pre-processing phase of the documents, we tried to use different methods like query expansion and re-ranking based on sentiment analysis. In the final part we report the results of our experiments and discuss about them and about other possible strategies that can be applied in the future.
Einzelmolekülexperimente zum Einfluss von Knoten und makromolekularem Crowding auf die Faltung von Proteinen

Mittels optischer Pinzette werden in dieser Arbeit zwei Effekte der Proteinfaltung an einzelnen Molekulen studiert: Durch Ziehen an verschiedenen Punkten des verknoteten Proteins UCH-L1 kann die Topologie des entfalteten Zustands kontrolliert und damit der Einfluss des Knotens auf die Faltungskinetik quantifiziert werden. Im zweiten Teil der Arbeit wird die Wirkung des entropischen Effekt des Ausschlussvolumens (makromolekulares Crowding) auf die Faltungskinetiken des Signalproteins Calmodulin untersucht.
Early Detection of Signs of Pathological Gambling, Self-Harm and Depression through Topic Extraction and Neural Networks

The eRisk track at CLEF 2021 comprised tasks on the detection of problem gambling and self-harm, and the assessment of the symptoms of depression. RELAI participated in these tasks through the use of topic extraction algorithms and neural networks. These approaches achieved strong results in the ranking-based evaluation of the pathological gambling and self-harm tasks as well as in the depression symptomatology task.
Correlation between optical characteristics and NO2 gas sensing performance of ZnO nanorods under UV assistance

Nitrogen oxides NOx (NO2, NO) are considered highly toxic gases, due to the adverse effects they have on human health as well as the environment. Thus, the analysis and control of NOx gases is extremely crucial. The gas sensors with high response, fast response-recovery times, and high selectivity with regard to NOx detection have attracted increased attention in recent times [1]. Nano metal oxide based NOx gas sensors constituent promising candidates for realistic application in this regard due to advantages such as extremely low detecting level (even up to ppb), high resolution, and fast response. For example, the gas sensors that utilized metal oxides, such as WO3 [2-4], ZnO [5-7], among others, were found to exhibit an extremely high sensing performance to NO2 gas. However, the metal oxide based gas sensors usually operate at high temperatures, and subsequently, become unstable or less reliable due to the changing particle size and morphology structure [8]. Therefore, the development of metal oxide gas sensors that operate at room temperature has been emphasized.
Diagnosis Model of Volleyball Skills and Tactics Based on Artificial Neural Network

With the development of China’s sports industry, the technical and tactical level of the team is required to be higher and higher. This study mainly discusses the diagnostic model of volleyball technique and tactics based on artificial neural network. With the help of the correlation function in Matlab neural network toolbox, in the training process of volleyball technical and tactical evaluation neural network, the sample data of volleyball technical evaluation index is repeatedly simulated and studied, and finally the network parameters with the minimum error and the highest accuracy are saved as the network model for subsequent verification and evaluation. The middle layer is the hidden layer, which makes the network approach the result of volleyball experts’ evaluation of the technology by adjusting the weights of neurons. The last layer is the output layer, which outputs the actual evaluation results of volleyball experts on the technology. Through repeated training and comparison of input samples, the maximum number of training times of BP network for volleyball technical and tactical evaluation is determined to be 32. Some common experience of estimating hidden node number is provided by trial-and-error method. On this basis, the number of hidden nodes to minimize the network error is finally determined to be 4 through repeated training and comparison. In the process of network diagnosis, the average difference between the evaluation score of network output and the score of actual experts is less than 1%, which reaches a very high precision. It shows that the volleyball skill evaluation model based on BP neural network is feasible in technology and the result is relatively reliable.
BlackOps at CheckThat! 2021: User Profiles Analyze of Intelligent Detection on Fake Tweets Notebook for PAN

An expensive task is fake news detection for recent trends among the concept of misinformation or rumors. In everywhere most of the times information lead or play emergent preface but forthwith misinformation also in everywhere to mislead the peoples mind and activity. Therefore, detecting fake content in any system can be a weapon over fictitious news. In any language cross over the exponential growth of fake news in social sites. Hence, it is the real time process to produce online fake news so that it has been needed to implement an automated technique whenever detect true from false. According to the solution of this approach made a research On English language textual inputs as twitter news from user profiles. At this point, due to accurate analysis for social media we experimented with supervised learning such as Decision tree, Random forest and gradient boosting. In between all the ML classifiers outperformed with 88% detection accuracy that mention the research of detection is more accurate.
Overview of ImageCLEFcoral 2021: Coral Reef Image Annotation of a 3D Environment

This paper presents an overview of the ImageCLEFcoral 2021 task that was organised as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2021. The task addresses the problem of automatically segmenting and labelling a collection of underwater images that can be used in combination to create 3D models for the monitoring of coral reefs. The training dataset contained 882 images from 6 subsets from 4 locations. 1 subset was complete (containing all the images to build the 3D model) and 5 subsets containing a partial collection. The test data (491 images) contained the images required to complete 4 of the partial image sets from each of the 4 locations (the final partial subset is not used for testing, only training). 8 teams registered to the ImageCLEFcoral task, of which 3 teams submitted 8 runs. Participants’ entries showed that although automatic annotation of benthic substrates was possible, developing a generic algorithm to work across multiple geographical locations will be difficult due to the variation of characteristics within and between classification types.