Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Detection of Sexism in Social Media with a Multilingual Approach

This paper describes the participation of the LaSTUS/TALN team in the shared task EXIST: sEXism Identiﬁcation in Social neTworks at IberLEF 2021. EXIST promotes the development of classiﬁcation systems able to spot sexism in social media posts in English and Spanish. More speciﬁcally a binary classiﬁcation task is proposed to classify posts in either sexist or not sexist, while a multi-class task challenges systems to identify the type of sexism present in the post. We address the two tasks with a transformers-based system applying a multi-lingual BERT model. The paper reports and discusses the results obtained by our team.
Scout-bot: Leveraging API Community Knowledge for Exploration and Discovery of API Learning Resources

Application Programming Interface (API) is a core technology that facilitates developers’ productivity by enabling the reuse of software components. Understanding APIs and gaining knowledge about their usage are therefore fundamental needs for developers. Here, API documentation plays a pivotal role in enabling developers to take full advantage of the benefits brought by APIs. The quality of API documentation has therefore become an important concern given the celerity and dynamics at which APIs are now being made available to users. This article aims at exploring existing research in the area of API documentation in order to identify the associated quality dimensions addressed by the literature. The research is carried out as a systematic mapping study where 103 research papers selected from the literature were reviewed and a total of 5 core quality dimensions were identified and analyzed. By focusing on the two most relevant quality dimensions (understandability and completeness), this article presents an approach to enable API users to explore, discover and learn about APIs through API topic issues discussed in Stack Overflow (SO). We demonstrate the feasibility of our approach through Scout-bot, our tool for exploration and discovery of API topic issues.
Your Eyes Explain Everything: Exploring the Use of Eye Tracking to Provide Explanations On-the-fly

Despite the proven advantages, explanations are not yet mainstream in industry applications of recommender systems. One of the possible reasons for this lack of adaption is the risk of overwhelming end-users with the explanations. In this paper, we investigate whether it is possible to overcome the information overload problem by only showing explanations that are relevant to the user. To do so, we leverage the gaze of a user as a novel responsiveness technique. We first conducted a co-design session to discuss several design decisions of a gaze responsive music recommender interface. As a next step, we implemented a gaze responsive music recommender interface and compared it in a between-subject user study (N=46) to two interfaces with more classical responsive techniques: a hover responsive interface and a click responsive interface. Our results show that providing explanations based on gaze is a promising solution to provide explanations on-the-fly.
Occupation Recognition and Normalization in Clinical Notes

This paper describes the system submitted in MEDical DOc-uments PROFessions recognition (MEDDOPROF) shared task, which is part of Iberian Languages Evaluation Forum (IberLeF) 2021 tasks. The Named Entity Recognition (NER) model built using Conditional Random Field, detects occupation and employment status entities in the Spanish medical documents. The entities are mapped to their code using vector embedding similarity of mention text with the code label text. The model obtains F-score of 0.635 for NER and 0.566 for the normalization task.
System Description for EXIST Shared Task at IberLEF 2021: Automatic Misogyny Identification Using Pretrained Transformers

This shared task system description depicts two neural network architectures submitted to the EXIST task at IberLEF 2021, among them the twelfth classiﬁed in the second sub-task. We present in detail the approach and topologies used to obtain the two systems which we submitted. Both systems are based on pretrained language models and solve the two subtasks simultaneously, with the ﬁrst system using diﬀer-ent networks for English and Spanish and the second using a multilingual approach.
A Visual SHACL Shapes Editor Based On OntoPad

On the Semantic Web, vocabularies and ontologies play a fundamental role to express the terminology and rules of certain domains. New technologies like SHACL provide the possibility to express data schemata specific to certain data sets, applications, and domains. However, the domain modeling process is collaborative and when using RDF, it requires technical knowledge. In this paper, we present a tool to support a two-step-process to model a terminology and a schema with a combined graphical RDF Schema editor and visual SHACL editor. This tool allows domain experts to create a terminology and schema without the need for a deep understanding of RDF Schema or SHACL.
Unsupervised Segmentation of Human Habits in Smart Home Logs through Process Discovery

Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence . An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences and needs. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose an unsupervised approach allowing, given a sensor log, to automatically segment human habits on a temporal basis, by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log.
A Data Discovery Platform Empowered by Knowledge GraphTechnologies: Challenges and Opportunities

In this talk, we present KGLac, a data discovery platform empowered by knowledge graph technologies, and highlights several open research challenges and opportunities. Reference Format: Essam Mansour. A Data Discovery Platform Empowered by Knowledge Graph Technologies: Challenges and Opportunities. In the 2nd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores (SEA Data 2021).
Semantic-Based Sports Music Information Fusion and Retrieval in Wireless Sensor Networks

The wireless sensor network has developed rapidly in recent years. It is formed by the intersection of multiple disciplines. It integrates embedded technology, sensor technology, distributed technology, wireless communication technology, and modern networks. It is a brand new information acquisition platform. The characteristics of sensor networks determine that information fusion technology is a hot spot in the research of wireless sensor networks. Information fusion can achieve high performance and low cost in terms of energy and communication, which is of great significance to the research of sensor networks. This paper is aimed at studying the semantic-based sports music information fusion and retrieval research in wireless sensor networks. WSNs may face various attacks including eavesdropping attacks, replay attacks, Sybil attacks, and DOS attacks. Therefore, they are designing sensor network solutions. It is necessary to consider the network security issues. This article summarizes and analyzes the existing WSN security data fusion solutions for this issue and compares them by classification. This paper proposes methods and theories such as the spatial correlation detection algorithm, CBA algorithm, FTD algorithm, and DFWD algorithm, which enriches the research of information fusion and retrieval in wireless sensor networks, which is of exploratory significance, and it also establishes this problem. The model was studied, and reliable data was obtained. The experimental results of this paper show that when using these methods to diagnose faults in WSN, the correct rate of model diagnosis is higher than 77%.
Fabrication of hollow nanoporous gold nanoshells with high structural tunability based on the plasma etching of polymer colloid templates

Recently, nanoporous gold nanoparticles have attracted great interest due to their optical and catalytic properties. However, most reports on their fabrication are based on the dealloying method. Here, we report a new fabrication technique for hollow nanoporous gold nanoshells (NPGNSs) with high structural tunability by sintering chemically synthesized gold nanoparticles (AuNPs) on the surface of polymer colloid particles. Size reduction of the colloid template by plasma etching induces contact of the AuNPs on the colloid surface. Elimination of the capping layers of AuNPs by plasma etching facilitates sintering of the contacted AuNPs. Complete removal of the colloid template after AuNP sintering results in hollow NPGNSs. A combination of AuNPs and polymer colloid templates of different sizes can change the structure and optical properties of hollow NPGNSs. Furthermore, AuNPs of different shapes, such as gold nanorods with a high aspect ratio and spherical AuNPs, can be used to make hollow NPGNSs, which gives more flexibility in tuning their structures. Our proposed fabrication technique based on shrinkage of templates and sintering of nanoparticles can be a new platform to prepare hollow nanoporous metal structures having nanoscale overall sizes with high structural tunability.
ColBERT at HAHA 2021: Parallel Neural Networks for Rating Humor in Spanish Tweets

Previously, we proposed ColBERT, a humor detection model based on the general linguistic structure of humor for formal English texts. ColBERT uses BERT model to produce embeddings for the text sentences, which will be put as inputs into a parallel neural network. In this paper, we utilized the proposed model on informal Spanish texts to detect humor and rate its level. The current task has three diﬀerences compared to the original humor detection task on the ColBERT dataset: (1) rating humor is a regression task rather than binary classiﬁcation, (2) texts are informal, and (3) texts are in a diﬀerent language. Using our general model and without any knowledge of the Spanish language, we participated in HAHA shared task at IberLEF 2021 Forum and achieved 2 nd place for humor rating and 3 rd place for binary humor detection. The results conﬁrm robustness of our proposed model.
A Collaborative, Realism-Based, Electronic Healthcare Graph: Public Data, Common Data Models, and Practical Instantiation

There is ample literature on the semantic modeling of biomedical data in general, but less has been published on realism-based, semantic instantiation of electronic health records (EHR). Reasons include difficult design choices and issues of data governance. A collaborative approach can address design and technology utilization issues, but is especially constrained by limited access to the data at hand: protected health information. Effective collaboration can be facilitated with public, EHR-like data sets, which would ideally include a large variety of datatypes mirroring actual EHRs and enough records to drive a performance assessment. An investment into reading public EHR-like data from a popular common data model (CDM) is preferable over reading each public data set’s native format. In addition to identifying suitable public EHR-like data sets and CDMs, this paper addresses instantiation via relational-to-RDF mapping. The completed instantiation is available for download, and a competency question demonstrates fidelity across all discussed formats.
Quest: A Query-driven Explanation Framework for Black-Box Classifiers on Tabular Data

Explainability efforts are well established in the ML and AI communities by now, with local, model-agnostic approaches currently being the tool of choice in information retrieval [2] and search [3] as well as many other areas. One major challenge in the field is the lack of sophisticated approaches for tabular/relational data, as opposed to text or images. Generic approaches, e.g. feature importance, limit expressiveness and readability. LIME [4] still remains the basis of many approaches for local, model-agnostic explanations. It was adapted to tabular data to serve as a baseline for Anchors [1]. Both approaches use a white-box model (surrogate) to approximate a black-box model locally in order to explain its decisions. However, their resulting explanations are limited: LIME (as implemented by the authors) focuses on feature importance. Anchors are derived as simple predicates to create if-then rules. Figure 1 shows examples for the UCI adult data set [5]. As an alternative we suggest Quest, a framework for
Strange Attractors in Writing Competence Development from the Perspective of Complexity Theory Based on Sensor Data

Writing competence is crucial for second language learners. Studying strange attractors in the development of writing competence is essential in understanding the laws of language development of foreign students. This study is aimed at investigating the state and laws of the development of Chinese as a second language (CSL) writing competence. Mathematical modeling and phase space construction methods in sensor research were used to investigate strange attractors in high-level Chinese learners studying in China in the development of CSL from the perspective of complexity theory based on the measurement framework of complexity, accuracy, and fluency. The results showed the following: (1) there are trends in the concentration and volatility of trigonometric function in different dimensions; (2) the group dynamic characteristics of writing development in CSL are simulated precisely by mathematical modeling; and (3) there are strange attractors with lexical density in CSL writing development. The development of CSL writing tends to maintain the state of strange attractors. The strange attractor reflects regularity in the dynamic, complex, and chaotic development of Chinese for international students, revealing the probabilistic prediction competence of different states in the development of CSL.
Extended Abstract: A Novel Mobile App for the Next Generation of Beekeepers

In this extended abstract, the authors report the ongoing work on a new mobile app for beekeepers. It is very important for beekeepers especially for those who are in search of new locations for their beehives to know the current situation in the immediate vicinity. This work presents a mobile application suitable for beekeepers to view weather and air quality data and, in particular, what people in the nearby area have annotated.
SiDi-NLP-Team at IDPT2021: Irony Detection in Portuguese 2021

This paper presents the submission of SiDi-NLP team in IDPT 2021 - Irony Detection in Portuguese (IberLEF 2021). Irony detection is a challenging semantic task similar to other tasks such as Sentiment Analysis. Due to these similarities we performed experiments using algorithms that achieved state-of-the-art results for similar semantic tasks in Brazilian Portuguese with linguistic feature representation and pre-trained BERT models applied to the two shared task datasets – Tweet Dataset and News Dataset. The pre-trained BERT models out-performed the other classiﬁers achieving 1 . 00 accuracy and F1 in the Tweet Dataset, and 0 . 903 accuracy/0 . 900 in F1 for the News Dataset. We also discuss the results considering the results obtained in the shared task.
TSIA team at FakeDeS 2021: Fake News Detection in Spanish Using Multi-Model Ensemble Learning

Fake news has become a hotly debated topic in journalism. This paper describes our contribution of the TSIA team in the Fake News Detection in Spanish Shared Task of IberLEF 2021. We regard this task as a binary classiﬁcation task. We mainly propose three model architectures based on the pre-trained model BETO and XLM-RoBERTa-Large. We ﬁrst ﬁne-tuned the Spanish pre-trained model BETO and then we chose the multi-language pre-trained model XLM-RoBERTa-Large to replace BETO and ﬁne-tune it, including the addition of CNN for feature extraction. Finally, our system achieves best F1-score of 0.6860 by hard voting, which ranks 10th out of 21 teams on the ﬁnal leaderboard. Our score is only 0.0806 worse than the best score on the leaderboard.
Attack Trees with Time Constraints

We propose how attack trees formalism can be extended with time constraints. An attack tree is a basic description of how an attacker can compromise an asset, we refine this basic description by adding time constraints which can prevent an attacker from reaching the root node, if the attack actions performed cannot be completed within the defined time constraint. Adding time to attack trees causes an infinite number of possible states, to overcome this problem, we translate the tree into (an extended version of) timed automata and later use UPPAAL verification tool to analyse.
Alignment Method of Combined Perception for Peg-in-Hole Assembly with Deep Reinforcement Learning

The method of tactile perception can accurately reflect the contact state by collecting force and torque information, but it is not sensitive to the changes in position and posture between assembly objects. The method of visual perception is very sensitive to changes in pose and posture between assembled objects, but they cannot accurately reflect the contact state, especially since the objects are occluded from each other. The robot will perceive the environment more accurately if visual and tactile perception can be combined. Therefore, this paper proposes the alignment method of combined perception for the peg-in-hole assembly with self-supervised deep reinforcement learning. The agent first observes the environment through visual sensors and then predicts the action of the alignment adjustment based on the visual feature of the contact state. Subsequently, the agent judges the contact state based on the force and torque information collected by the force/torque sensor. And the action of the alignment adjustment is selected according to the contact state and used as a visual prediction label. Whereafter, the network of visual perception performs backpropagation to correct the network weights according to the visual prediction label. Finally, the agent will have learned the alignment skill of combined perception with the increase of iterative training. The robot system is built based on CoppeliaSim for simulation training and testing. The simulation results show that the method of combined perception has higher assembly efficiency than single perception.
RUDI: Real-Time Learning to Update Dense Retrieval Indices

Dense retrieval models demonstrate great effectiveness gains for retrieval and re-ranking by learning a vector space embedding for the queries and the documents in the corpus [1, 2, 3, 4, 5, 6]. At the same time dense retrieval improves inference speed at query time with fast approximate nearest neighbor search [7, 8, 9] compared to exact k-nearest neighbor search by moving most of the computational effort to the indexing phase. For search indices in production, there are continously millions of new data points which need to be included in the index in real-time [10, 11]. With the constantly new content to be indexed, the overall content of the whole corpus shifts gradually. With this it also shifts what should be considered relevant for a given query. For example the global COVID-19 pandemic resulted in an explosion of COVID-19 related websites, news articles and scientific publications [12]. In order to track, understand and seek this rapidly growing, novel information, the information retrieval community created a continously growing research dataset containing COVID19 related publications [13]. To be able to find high-quality, relevant, and recent results, the novel content not only needs to be included in the search index, but the indexing model needs to account for the content shift and update the index in real-time. To incorporate this content shift in real-time in production systems, the dense retrieval model needs to be re-trained and the corpus re-indexed with the re-trained dense retrieval model. Real-time user interactions provide labels for re-training the dense retrieval model in real-time. However updating the search index in real-time remains an open challenge. In production systems the search indices have a size up to 100 millions of terabytes, thus re-indexing the whole corpus is computationally expensive and not feasible in real-time scenarios. In this paper we propose the concept RUDI for Realtime learning to Update Dense retrieval Indices with
Recognition Technology of Athlete's Limb Movement Combined Based on the Integrated Learning Algorithm

Human motion recognition based on inertial sensor is a new research direction in the field of pattern recognition. It carries out preprocessing, feature selection, and feature selection by placing inertial sensors on the surface of the human body. Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis. Accurately identifying these movement modes is of great significance for swing movement analysis. With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual sensors. However, there is not much work on movement recognition for table tennis, and the methods are still mainly integrated into the traditional field of machine learning. Therefore, this paper uses an acceleration sensor as a motion recording device for a table tennis disc and explores the three-axis acceleration data of four common swing motions. Traditional machine learning algorithms (decision tree, random forest tree, and support vector) are used to classify the swing motion, and a classification algorithm based on the idea of integration is designed. Experimental results show that the ensemble learning algorithm developed in this paper is better than the traditional machine learning algorithm, and the average recognition accuracy is 91%.
Bribones tras la esmeralda perdida@FakeDeS 2021: Fake news detection based on random forests, k-nearest neighbors, and n-grams for a Spanish corpora

Fake news constitutes a social problem that aﬀects democratic societies by inﬂuencing and manipulating public opinion. The concept of fake news covers a wide range of news that somehow resembles the truth. For example, they might contain explicit false assertions, contain some aspects of the truth, or even present the truth in a deformed fashion to disqualify a political ﬁgure or a speciﬁc group. The obnoxious impact of fake news makes it imperative to develop tools to help professional journalists, on the one hand, and the public, on the other, to detect news that is fake. In this contribution, we describe a methodology with the aim to tell apart fake from true news in a data set of several hundreds of manually curated journalistic pieces. The proposal is based on a bag-of-words approach and relies on shallow classiﬁers. We intended to validate the hypothesis that true and fake news can be told apart with simple assumptions. However, as discussed, the hypothesis was rejected, as it was not possible to classify the texts in a subsequent test stage successfully. The corpora used for the classiﬁcation task is in Spanish, and it was presented as an open challenge in CodaLab.