Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

: One of the major security challenges in cloud computing is distributed denial of service (DDoS) attacks. In these attacks, multiple nodes are used to attack the cloud by sending huge traﬀic. This results in the unavailability of cloud services to legitimate users. In this research paper, a hybrid machine learning-based technique has been proposed to detect these attacks. The proposed technique is implemented by combining the extreme learning machine (ELM) model and the blackhole optimization algorithm. Various experiments have been performed with the help of four benchmark datasets namely, NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, to evaluate the performance of our proposed technique. It achieves an accuracy of 99.23%, 92.19%, 99.50%, 99.80% with NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, respectively. The performance comparison with other techniques based on ELM, artificial neural network (ANN) trained with blackhole optimization, backpropagation ANN, and other state-of-the-art techniques is also performed.
Applying Pre-trained Model and Fine-tune to Conduct Humor Analysis on Spanish Tweets

This paper describes in detail the four subtasks of HAHA@ IberLEF 2021 [7] : Humor Analysis based on Human Annotation. Sub-task 2 is a regression problem, and the other three subtasks are all text classiﬁcation problems. The data comes from the Twitter social platform, and the language is Spanish. The classiﬁcation problem is mainly solved by integrating the Multilingual Bert model and the LSTM model, and the regression problem is solved by the GPT-2 Model. According to the oﬃcial evaluation results, the result of the method proposed in this paper ranks fourth, eighth, ﬁfth, and sixth on the four subtasks, respectively. For this task, I have uploaded the code to GitHub kuiyongyi for easy reference by others.
Exploration of Spanish Word Embeddings for Lexical Simplification

Lexical simplification systems rely heavily on handcrafted databases or parallel corpora which represents a high cost of production. In this paper we present alternatives for every step in the lexical simplification process individually for the Spanish language by exploring the potential that word embeddings can offer. This study covers the entire pipeline in lexical simplification, from the task of complex word identification (CWI) to substitute generation, selection and ranking (SG/SS/SR). Taking advantage of the different applications of BERT models, we fine-tune two pre-trained models to detect unusual words with the help of available Spanish datasets. Next, we compare features that different types of embedding can give to find the best candidate for replacement for a target word. The resulting models in the CWI step show a fair result compared to other systems that used the same datasets. Also, we found better results than previous works by analyzing the similarity of words in context when evaluating embedding models.
UMUTeam at EXIST 2021: Sexist Language Identification based on Linguistic Features and Transformers in Spanish and English

Sexism is harmful behaviour that can make women feel worth-less promoting self-censorship and gender inequality. In the digital era, misogynists have found in social networks a place in which they can spread their oppressive discourse towards women. Although this particular form of oppressive speech is banned and punished on most social networks, its identiﬁcation is quite challenging due to the large number of messages posted everyday. Moreover, sexist comments can be unnoticed as condescends or friendly statements which hinders its identiﬁcation even for humans. With the aim of improving automatic sexist identiﬁ-cation on social networks, we participate in EXIST-2021. This shared task involves the identiﬁcation and categorisation of sexism language on Spanish and English documents compiled from micro-blogging platforms. Speciﬁcally, two tasks were proposed, one concerning a binary classiﬁcation of sexism utterances and another regarding multi-class identiﬁcation of sexist traits. Our proposal for solving both tasks is grounded on the combination of linguistic features and state-of-the-art transformers by means of ensembles and multi-input neural networks. To address the multi-language problem, we tackle the problem independently by language to put the results together at the end. Our best result was achieved in task 1 with an accuracy of 75.14% and 61.70% for task 2.
Enhancing SNS ProfileWriting with a Search-Based Assistant System

In Social Networking Services (SNS), user profiles, which often consist of an image, texts, and other items, have an important role to connect with other users. However, in a preliminary study with 3,193 sample profiles on Twitter, we found that the average length of profile texts was 40 characters ( 𝑆𝐷 = 32 . 52 ) where the maximum length is 160. This suggests that many SNS users are missing potential opportunities to expand their social network due to the short profile texts. Therefore, we proposed a search-based interactive system to support the writing of profile texts in SNS. The proposed system was designed to dynamically search for similar profile texts while users were typing their profile so that they can get new ideas such as what to write or how to express. We evaluated the effect of the proposed system by a user study with 24 participants, and found that the proposed system enabled participants to significantly increase the length of written profile texts (+92.5% on average), compared to a baseline system with no assistance.
Emotion Classification for Spanish with XLM-RoBERTa and TextCNN

On social networking platforms, users usually cannot perceive each other’s speech tone and facial expressions, making the emotions conveyed by users on the Internet not clear enough. This task is Spanish emotion detection and evaluation from EmoEvalEs@IberLEF 2021. Speciﬁcally, the task includes dividing the emotions expressed on Twitter into one of the following seven categories: Anger, Disgust, Fear, Joy, Sadness, Surprise or Others. Our team (team name is Dong) ﬁrst use XLM-Roberta for embedding. Then we input the word vector into Transformer Encoder for the secondary extraction of features, and then input the result into TextCNN. Using TextCNN’s ability to capture local features, our model could extract high-level features such as text semantics, word order, and context. Finally, the output of the model is input into the fully connected layer for classiﬁcation. Our model rank 14th in this task. The weighted-averaged F1 is 0.5570, and the accurcy is 0.5368.
Question Answering on RDF Data based on Grammars Automatically Generated from Lemon Models

Many question answering (QA) systems over RDF induced from question-query pairs using some machine learning technique suffer from a lack of controllability, making the governance and incremental improvement of the system challenging, not to mention the initial effort of collecting and providing training data. As an alternative, we present a model-based QA approach that uses an ontology lexicon in lemon format and automatically generates a lexicalized grammar used to interpret and parse questions into SPARQL queries. The approach gives maximum control over the QA system to the developer as every lexicon extension increases the coverage of the grammar, and thus of the QA system, in a predictable way. We describe our approach to generating grammars from lemon lexica and show how these grammars generate speciﬁc questions that we index to support fast QA performance in a prototype that answers questions with respect to DBpedia.
Challenges in Legal Process Discovery

One of the main promises of process conformance is the op-portunity to align normative processes (i.e. how the process should behave) and event logs (i.e. how does the process actually behaves). Results of conformance checking are valid as long as normative processes correspond to actual norms. Recent developments advocate the use of Natural Language Processing (NLP) to process model discovery from texts. We present a series of challenges in textual process discovery that limit its applicability to real norms. The challenges emerges from experiences with legal practitioners in the digitalization of administrative processes in Danish and Italian municipalities, and they need to be solved in order to provide accurate normative processes that reflect the intent of laws.
An Embeddings Based Recommendation System for Mexican Tourism. Submission to the REST-MEX Shared Task at IberLEF 2021

REST-MEX 2021 ( Recommendation System for Text Mexican Tourism) is one of the IberLEF 2021 tasks, dedicated to generate recommendation systems for tourist sites based on an user’s proﬁle’s aﬃnity compared to each place description. Considering the importance of tourism in the economy, it is vitally important to generate Spanish resources that allow the generation of systems that help to develop intelligent systems in tourism. Considering the above, we proposed a system based on distributed representations of texts, using the BERT approach. We did not use any handcrafted features or external datasets as prior information.
Temporal Relation Extraction: The Event Ordering Task

Although most Natural Language Processing tasks, such as Text Classification and Natural Language Translation, have experienced a major performance improvement due to recent advances in neural network architectures, Temporal Relation Extractionremainsanopenchallenge. Thisleavesthedooropenfornewresearchquestions. Inthispaper, weprovideabrief summary of the task and some of the recent efforts that have been made to solve it. In addition, some research opportunities yet to be explored are also discussed.
IberLEF 2021 Overview: Natural Language Processing for Iberian Languages

Resumen IberLEF is a comparative evaluation campaign for Natural Language Processing Systems in Spanish and other Iberian languages. Its goal is to encourage the research community to organize competitive text processing, understanding and generation tasks in order to deﬁne new research challenges and set new state-of-the-art results in those languages. This paper summarizes the evaluation activities carried out in IberLEF 2021, which included twelve tasks dealing with emotions, stance and opinions, harmful information, health-related information extraction and discovery, humor and irony, and lexical acquisition. Overall, IberLEF activities were a remarkable collective eﬀort involving 359 researchers from 22 countries in Europe, Asia and the Americas.
MB-Courage @ EXIST: GCN Classification for Sexism Identification in Social Networks

We describe our approach (MB-Courage team) in the Sexism Identiﬁcation in Social Networks Shared Task (EXIST). We submitted three runs for each task, two of them based on Graph Convolutional Neural Networks (GCN) exploring diﬀerent edge creation strategies and one combining graph embeddings from diﬀerent GCN through ensemble methods. In addition, we explored diﬀerent GCN models and text-to-graph strategies. We identiﬁed that in Task 2 the models take advantage of the syntactic relationship between words encoded in the graph, while it did not strongly impact Task 1. Moreover, the models generalized the task while maintaining similar (in some cases better) results in the social network that was not used in training. On average, our best models performed similarly across languages and social media, ranking 37th (out of 72 runs) for Task 1 and 40th (out of 63) for Task 2.
Research on Land Use Change and Ecological Environment Effect Based on Remote Sensing Sensor Technology

In the regional landscape pattern, woodland landscape patches are developed in a regular manner, showing a trend of agglomeration as a whole; the concentration and dominance of landscape patches of cultivated land, grassland, and unused land are decreasing, and the overall situation is fragmented; the water landscape pattern is also showing a regular development state to varying degrees. On the whole, the landscape of a certain area is scattered and fragmented, and the dominant landscape types dominate the overall landscape layout of the region. The diversity of land use landscape pattern shows positive autocorrelation as a whole. According to the Moran’s I index of the diversity of land use landscape patterns, the overall agglomeration effect of the region has increased, but the overall trend is in a balanced trend. The ecological service value of urban land use change continues to increase, but the regional differences are significant, the structure is simple, and it is mainly supported by ecosystems such as forest land, water area, and agricultural land. Based on the analysis of the spatiotemporal evolution of land use, an in-depth analysis of the urban water environment, atmospheric environment, soil environment, urban heat island, and other environmental issues of the urban land use in the study area is carried out. The calculation and analysis results of the ecological service value of urban land use change and its flow direction change show that the ecosystem service value has increased, and the regional difference and flow direction of the ecosystem service value have changed significantly.
Designated-Verifier Anonymous Credential for Identity Management in Decentralized Systems

Most of the existing identity management is the centralized architecture that has to validate, certify, and manage identity in a centralized approach by trusted authorities. Decentralized identity is causing widespread public concern because it enables to give back control of identity to clients, and the client then has the ability to control when, where, and with whom they share their credentials. A decentralized solution atop on blockchain will bypass the centralized architecture and address the single point of the failure problem. To our knowledge, blockchain is an inherited pseudonym but it cannot achieve anonymity and auditability directly. In this paper, we approach the problem of decentralized identity management starting from the designated-verifier anonymous credential (DVAC in short). DVAC would assist to build a new practical decentralized identity management with anonymity and auditability. Apart from the advantages of the conventional anonymous credential, the main advantage of the proposed DVAC atop blockchain is that the issued cryptographic token will be divided into shares at the issue phase and will be combined at the showing credential phase. Further, the smooth projective hash function ( SPHF in short) is regarded as a designated-verifier zero-knowledge proof system. Thus, we introduce the SPHF to achieve the designated verifiability without compromising the privacy of clients. Finally, the security of the proposed DVAC is proved along with theoretical and experimental evaluations.
It's Messy Out There: DBC's Journey Towards its First Test Collection

The use of test collections to evaluate the effectiveness of Information Retrieval (IR) systems is wide-spread, and the literature covers many examples of improvements and ways to solve specific problems. In this abstract, we explore the initial difficulties of building and implementing a test collection outside of academic walls and without having easy or immediate access to many of the main tools discussed in academic papers. This is an example of how a test collection can be a way to engage companies in finding creative solutions to make a first step towards the evaluation of IR systems.
UMUTeam at HAHA 2021: Linguistic Features and Transformers for Analysing Spanish Humor. The What, the How, and to Whom

Giving computers basic notions of humour can result in better and more emphatic user interfaces that are perceived more natural. In addition, hate-speech systems that understand humor are more reliable, as humor can be used as a Troy Horse to introduce oppressive-speech passing them oﬀ as harmless jokes. Understanding humor, however, is challenging because it is subjective as well as cultural and background dependant. Sharpen forms of humor, moreover, rely on ﬁgurative language, in which words loss their literally meaning. Therefore, humor detection has been proposed as shared-task in workshops in the last years. In this paper we describe our participation in the HAHA’2021 shared task, regarding ﬁne-grain humor identiﬁcation in Spanish. Our proposals to solve these subtasks are grounded on the combination of linguistic features and transformers. We achieved the 1st position for humor rating Funniness Score Prediction with a RMSE of 0.6226, the 8th position for humor classiﬁcation subtask with an 85.44 F1-score of humours category, and the 7th and the 3rd position for the subtasks of humor mechanism and target classiﬁcation with an macro averaged F1-score of 20.31 and 32.25 respectively.
COVIDGraph: Connecting Biomedical COVID-19 Resources and Computational Biology Models

The COVID-19 pandemic has changed life across the globe. In January 2020, little was known about SARS-COV-2, but the vastly increasing number of infections and the uncontrolled spreading demanded fast medical action. Within a year, over 4 million publications relating to COVID-19 appeared in the scientific literature. Additionally, patents have been registered, ontologies have been extended, simulation studies for prediction of disease spread and underlying bioinformatics mechanisms have been built, and health studies have been designed. To support the exploration of COVID-19 data, the CovidGraph project was initiated as a non-profit, collaborative and open project driven by researchers, software developers, data scientists and medical professionals. In this article we outline the history, goals and scope of CovidGraph. Using the example of computational biology models, we show how additional resources can be integrated with the knowledge graph to extend the scope of the CovidGraph, for example, to systems biology data. © 2021 CEUR-WS. All rights reserved.
HAHA at EmoEvalEs 2021: Sentiment Analysis in Spanish Tweets with Cross-lingual Model

This article describes HAHATeam participation in the Iber-LEF 2021 EmoEvalEs task: Emotion detection and Evaluation for Spanish. In this task, we use Masked Language Model-based data augmentation to enhance the data to increase the training data set and prevent overﬁtting. We improve the performance of the model through a series of data processing and data augmentation technologies. We use three diﬀerent cross-language models, BERT, XLM, and XLM-RoBERTa for comparative experiments. Very competitive results have been achieved on both the development set and the test set. The best model achieved the third place in the development set and the ﬁfth place in the test set.
A linear programming approach to multiple instance learning

: Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wide range of real-world data mining applications. In MIL, objects are represented by a bag of instances instead of a single instance, and class labels are provided only for the bags. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all remaining instances are negative. This study proposes a linear programming framework to learn instance level contributions to bag label without emposing the standart assumption. Each instance of a bag is mapped to a pseudo-class membership estimate and these estimates are aggregated to obtain the bag-level class membership in an optimization framework. A simple linear mapping enables handling various MIL assumptions with adjusting instance contributions. Our experiments with instance-dissimilarity based data representations verify the effectiveness of the proposed MIL framework. Proposed mathematical models can be solved eﬀiciently in polynomial time.
A Well-founded Graph-based Summarization Framework for Description Logics

The quotient operation from graph theory offers an elegant graph summarization framework that has been widely investigated in the literature, notably for the exploration and efficient management of large graphs; it consists in fusing equivalent vertices according to an equivalence relation. In this paper, we study whether a similar operation may be used to summarize ABoxes. Towards this goal, we define and examine the quotient operation on an ABox: we establish that a quotient ABox is more specific than the ABox it summarizes, and characterize to which extent it is more specific. This preliminary investigation validates the interest of a quotient-based ABox summarization framework, and paves the way for further studies on it in the description logic setting, e.g., to devise equivalence relations suited to the optimization of typical data management and reasoning tasks on large ABoxes or to the visualization of large ABoxes, and on its utilization in related settings, e.g., Semantic Web.
Using Lexical Resources for Detecting Offensiveness in Mexican Spanish Tweets

This work presents a description of our participation in sub-tasks 3 and 4 at MeOﬀendEs@IberLEF 2021 which consisted in classifying tweets as oﬀensive or non-oﬀensive in the OﬀendMEX corpus. For both subtasks, we proposed to use several Spanish lexicons which have a collection of words that have been weighted according to diﬀerent criteria like aﬀective, dimensional, and emotional values. In addition to them, structural values, word-embeddings and one-hot codiﬁcation were taken into account. The scores of recall metric obtained in both subtasks was competitive comparing to both the baseline of the competition’s and the other teams’.
Factors that influence primary school teachers when sending homework to students in the Region of Murcia (Spain)

The aim of this article is to define factors that influence elementary school teachers when assigning homework to students. The study was conducted with a sample of 93 teachers of the Region of Murcia (Spain). Teachers answered a questionnaire, with 35 questions, that was validated through an expert judgment by the Angoff method (Ricker, 2006). The analysis of data defined the factors, and they were: Family Involvement, Personal Feelings, Staff Decisions and Assumptions. Besides, the variables defined as frequency of homework, activities per week and needed to do homework, help us to conclude that there were decisive factors for teachers when giving homework to their students, and these factors were conditioned by the number of days per week that teachers assign tasks to their students, the estimated number of activities per week given to students, and the time needed to complete the tasks.
Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

The decision-making process, promptly on time, is a crucial success factor in large organizations. Generally, the data warehouses of these organizations grow rapidly with the data generated from various business activities. This huge volume of data needs to be analyzed and decisions must be made quickly to meet the market challenges. Accurate knowledge extraction and its visualization from big data can guide decisionmakers to conduct key analysis and make correct predictions. This paper proposes a decision-making framework that not only takes into account knowledge extraction and visualization but also considers the security of the data. The proposed framework uses data mining techniques to extract useful patterns, then, visualizes those patterns for further analysis and decision making. The significance of the proposed framework lies in the mechanism through which it protects the data from intruders. The data is first processed and then stored in an encrypted format on the cloud. When the data is needed for analysis and decision making, a temporary copy of the data is first decrypted, and then important patterns are visualized. The proposed framework will assist managers and other decision-makers to analyze and visualize the data in real-time with an enhanced