Miracle-dz/UniFAR-FTUs huggingface dataset:
PAR-MEX Shared Task Submission Description: Identifying Spanish Paraphrases Using Pretrained Models and Translations

This paper describes our participation in the Paraphrase Identification in Mexican Spanish (PAR-MEX) shared task [1]. We show that publicly available, pretrained models already achieve very strong performance on this task (ranking 2nd among all submissions and achieving 0.9373 f1 score on test data) and can be used in a data efficient way. The performance is good despite domain specificity of the data and linguistic differences, e.g. Mexican Spanish vs. standard Spanish. Furthermore, we show that translation to English allows for using further pretrained models with stronger performance. We conclude that combining different pretrained and multilingual models is a currently strong and efficient approach to paraphrase identification, and paraphrase identification will likely benefit more from stronger pretrained models than from specialised methods.
Alejandro Mosquera at PoliticEs 2022: Towards Robust Spanish Author Profiling and Lessons Learned from Adversarial Attacks

Social media publications can inadvertently reveal a broad amount of potentially sensitive information such as gender, age, ethnicity or political ideology even when are not intentionally disclosed by their authors. For this reason, social media users concerned about the fact that Natural Language Processing techniques can infer some of these traits with a relatively high degree of accuracy, may actively attempt to conceal the information that they do not want to share for profiling purposes. This paper both describes the user profiling system submitted to the IberLEF 2022 task PoliticEs: “Spanish Author Profiling for Political Ideology” and evaluates its robustness by simulating adversarial perturbations. The aforementioned system was ranked 3rd overall in the shared task with a 88.9% F1, just 1.3 percentage points lower than the highest scoring team. Empirical results suggest that some of the avoidance techniques explored in this research are also likely to successfully evade similar NLP-based author profiling approaches.
HalBERT at PoliticEs 2022: Are Machine Learning Algorithms better for Author Profiling?

This paper presents an investigation of different machine and deep learning models for Author Profiling for the Shared Task PoliticEs: Spanish Author Profiling for Political Ideology at IberLEF 2022. We provide a broad comparative study to see where both approaches stand. Our analysis show that ML methods are more effective than DL methods. Overall, our system was ranked 5th on the leaderboard based on the average macro F1-score across all sub-tasks.
The Unified Surface Ricci Flow

Ricci flow deforms the Riemannian metric proportionally to the curvature, such that the curvature evolves according to a heat diffusion process and eventually becomes constant everywhere. Ricci flow has demonstrated its great potential by solving various problems in many fields, which can be hardly handled by alternative methods so far. This work introduces the unified theoretic framework for discrete Surface Ricci Flow, including all common schemes: Thurston's Circle Packing, Tangential Circle Packing, Inversive Distance Circle Packing and Discrete Yamabe. Furthermore, this work also introduces a novel scheme, virtual radius circle packing, under the unified framework. This work gives explicit geometric interpretation to the discrete Ricci energy for all the schemes, and Hessian of the discrete Ricci energy for schemes with Euclidean back ground geometry. The unified frame work deepen our understanding to the the discrete surface Ricci flow theory, and inspired us to discover the new schemes, improved the flexibility and robustness of the algorithms, greatly simplified the implementation and improved the debugging efficiency. Experimental results shows the unified surface Ricci flow algorithms can handle general surfaces with different topologies, and is robust to meshes with different qualities, and effective for solving real problems.
Exploring Representation of Horn clauses using GNNs

Learning program semantics from raw source code is challenging due to the complexity of real-world programming language syntax and due to the difficulty of reconstructing long-distance relational information implicitly represented in programs using identifiers. Addressing the first point, we consider Constrained Horn Clauses (CHCs) as a standard representation of program verification problems, providing a simple and programming language-independent syntax. For the second challenge, we explore graph representations of CHCs, and propose a new Relational Hypergraph Neural Network (R-HyGNN) architecture to learn program features. We introduce two different graph representations of CHCs. One is called constraint graph (CG), and emphasizes syntactic information of CHCs by translating the symbols and their relations in CHCs as typed nodes and binary edges, respectively, and constructing the constraints as abstract syntax trees. The second one is called control- and data-flow hypergraph (CDHG), and emphasizes semantic information of CHCs by representing the control and data flow through ternary hyperedges. We then propose a new GNN architecture, R-HyGNN, extending Relational Graph Convolutional Networks, to handle hypergraphs. To evaluate the ability of R-HyGNN to extract semantic information from programs, we use R-HyGNNs to train models on the two graph representations, and on five proxy tasks with increasing difficulty, using benchmarks from CHC-COMP 2021 as training data. The most difficult proxy task requires the model to predict the occurrence of clauses in counter-examples, which subsumes satisfiability of CHCs. CDHG achieves 90.59% accuracy in this task. Furthermore, R-HyGNN has perfect predictions on one of the graphs consisting of more than 290 clauses. Overall, our experiments indicate that R-HyGNN can capture intricate program features for guiding verification problems.
Combining Linear Regressions to Determine the Future of the Covid in Mexico from the News

This paper presents an approach to determine the Semaphore Covid in Mexico from the news to participate in the Rest-Mex 2022 evaluation forum. The purpose of the task is to determine the covid semaphore color (red, orange, yellow, and green) in different time spaces. The proposed approach consists of two main steps. First, to generate a list of topics of the news, and second, to implement several linear regressions methods in order to these results serve to feed a deep neural network. For the first step, the LDA algorithm was implemented, and for the second, well-known methods such as Lasso, Ridge, Lars, among others, were utilized. With this approach, a weighted average of 0.48 was obtained, which is considerably higher than the baseline proposed by the organizers, which is 0.12. The best result to classify the semaphore was two weeks in the future with 0.56 of F-measure. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Higher-Order Quantified Boolean Satisfiability

The Boolean satisfiability problem plays a central role in computational complexity and is often used as a starting point for showing NP lower bounds. Generalisations such as Succinct SAT, where a Boolean formula is succinctly represented as a Boolean circuit, have been studied in the literature in order to lift the Boolean satisfiability problem to higher complexity classes such as NEXP. While, in theory, iterating this approach yields complete problems for k -NEXP for all k > 0, using such iterations of Succinct SAT is at best tedious when it comes to proving lower bounds. The main contribution of this paper is to show that the Boolean satisfiability problem has another canonical generalisation in terms of higher-order Boolean functions that is arguably more suitable for showing lower bounds beyond NP. We introduce a family of problems HOSAT( k, d ), k ≥ 0, d ≥ 1, in which variables are interpreted as Boolean functions of order at most k and there are d quantifier alternations between functions of order exactly k . We show that the unbounded HOSAT problem is TOWER-complete, and that HOSAT( k, d ) is complete for the weak k -EXP hierarchy with d alternations for fixed k, d ≥ 1 and d odd. We illustrate the usefulness of HOSAT by characterising the complexity of weak Presburger arithmetic, the first-order theory of the integers with addition and equality but without order. It has been a long-standing open problem whether weak Presburger arithmetic has the same complexity as standard Presburger arithmetic. We answer this question affirmatively, even for the negation-free fragment and the Horn fragment of weak Presburger arithmetic. Boolean functions, weak k -EXP hierarchies, non-elementary complexity, Presburger arithmetic
INFOTEC-LaBD at PoliticES 2022: Low-dimensional Stacking Model for Political Ideology Profiling

This manuscript describes the low-dimensional stacking model approach used to profile users to solve gender and profession identification and binary and multiclass ideology prediction tasks. We developed these models in the scope of the PoliticEs: Spanish Author Profiling for Political Ideology carried out at IberLEF@SEPLN 2022 . Our methodology stacks several low-dimensional representations that can be used to visualize the dataset and as the input dataset for a classifier. While the results were late in the challenge, our final evaluations achieved high performances in the training and test partitions. We believe they are promising approaches on the road to creating transparent and competitive user profiling models.
Consensus: bridging theory and practice

Distributed consensus is fundamental to building fault-tolerant systems. It allows a collection of machines to work as a coherent group that can survive the failures of some of its members. Unfortunately, the most common consensus algorithm, Paxos, is widely regarded as difficult to understand and implement correctly. This dissertation presents a new consensus algorithm called Raft, which was designed for understandability. Raft first elects a server as leader, then concentrates all decision-making onto the leader. These two basic steps are relatively independent and form a better structure than Paxos, whose components are hard to separate. Raft elects a leader using voting and randomized timeouts. The election guarantees that the leader already stores all the information it needs, so data only flows outwards from the leader to other servers. Compared to other leader-based algorithms, this reduces mechanism and simplifies the behavior. Once a leader is elected, it manages a replicated log. Raft leverages a simple invariant on how logs grow to reduce the algorithm’s state space and accomplish this task with minimal mechanism. Raft is also more suitable than previous algorithms for real-world implementations. It performs well enough for practical deployments, and it addresses all aspects of building a complete system, including how to manage client interactions, how to change the cluster membership, and how to compact the log when it grows too large. To change the cluster membership, Raft allows adding or removing one server at a time (complex changes can be composed from these basic steps), and the cluster continues servicing requests throughout the change. We believe that Raft is superior to Paxos and other consensus algorithms, both for educational purposes and as a foundation for implementation. Results from a user study demonstrate that Raft is easier for students to learn than Paxos. The algorithm has been formally specified and proven, its leader election algorithm works well in a variety of environments, and its performance is equivalent to Multi-Paxos. Many implementations of Raft are now available, and several companies are deploying Raft.
Nucleolus-Associated Localization of J Chains in IgG 1 (X)-Producing Myeloma Cells

By using both direct and indirect immunofluorescence technics and the PAP method, the authors observed an unusual immunocytochemical staining pattern in myeloma cells producing IgG 1(X). J chains were localized mainly as intranuclear spots, compared with y and X chains that are diffusely distributed in the cytoplasm with dense perinuclear accumulation. The present findings on the location, size, and number of the intranuclear spots suggest that J chains are nucleolus associated. The authors hypothesize that J chains are synthesized in the cytoplasm and transported into the nucleolus, where they associate with nucleolar proteins, or that they are synthesized within the nucleolus, which participates in biogenesis of ribosomes. Am J Clin Pathol 1985; 83: 577-581 melphalan, and gluco corticoids. Skeletal X-ray films disclosed numerous lytic lesions on the skull. Bone marrow aspiration revealed 41.2% abnormal plasma cell, with a marked atypism. The total serum protein was 14 g/dL, and cellulose acetate membrane electrophoresis revealed a monoclonal spike that showed 60.5% y mobility and 26.3% albumin. IgG was 9,135 mg/dL; IgA, 22 mg/dL; and IgM, 21 mg/dL. A monoclonal spike in the serum was identified immunologically as IgG 1(A) char acterized by hyperviscosity.
Lak_NLP at IberLEF-2022 DETESTS task: Automatic Classification of Stereotypes in Text

This article briefly explains how the Lak_NLP team approached the problem of detecting racial stereotypes. This project involves two tasks. The first is to classify a text according to whether or not it presents racial stereotypes, the second subtask is to assign to those sentences ten categories of racial stereotypes which are: 1) “victims of xenophobia”, 2) “suffering victims”, 3) “economic resources”, 4) a problem of “immigration control”, 5) people with “cultural and religious differences”, 6) people who benefit from our social policy, 7) a problem for “public health”, 8) a threat to “security”, 9) “dehumanization” and 10) “other” types of stereotypes. By developing and studying various classifiers based on the Transformers architecture, namely BERT and BETO, we will address the task of detecting racial stereotypes.
Institutional Knowledge at Singapore Management University Institutional Knowledge at Singapore Management University Aligning images in the wild Aligning images in the wild

Aligning image pairs with signiﬁcant appearance change is a long standing computer vision challenge. Much of this problem stems from the local patch descriptors’ instability to appearance variation. In this paper we suggest this instability is due less to descriptor corruption and more the difﬁculty in utilizing local information to canonically deﬁne the orientation (scale and rotation) at which a patch’s descriptor should be computed. We address this is-sue by jointly estimating correspondence and relative patch orientation, within a hierarchical algorithm that utilizes a smoothly varying parameterization of geometric transformations. By collectively estimating the correspondence and orientation of all the features, we can align and orient features that cannot be stably matched with only local information. At the price of smoothing over motion discontinuities (due to independent motion or parallax), this approach can align image pairs that display signiﬁcant inter-image appearance variations.
UNED at PoliticEs 2022: Testing Approximate Nearest Neighbors and Spanish Language Models for Author Profiling in Political Ideology

PoliticES at IberLef 2022 proposes to profile users in a political domain according to their tweets. In our participation, we have tested two different approaches and some combinations of them. Our first approach is based on approximate nearest neighbours, which obtains low scores for individual results but a great score when combining several outputs. On the other hand, we have also tested some BERT-based systems to study the performance of such technologies in this task. While these systems obtain the best score of our submissions (0.74), we still want to study how to combine them with the first approach to take advantage of the best features of each approach.
Named Entity Recognition For Humans and Species With Domain-Specific and Domain-Adapted Transformer Models

This work presents different solutions to the tasks proposed at the LivingNER challenge, as part of the Iberlef 2022 Conference, with a special focus on the NER task. For that, a general domain large model was adapted to the biomedical domain, showing that this process improves the posterior fine-tuning on a majority of tasks. However, although achieving similar results, it is not able to outperform two base size models specific of the biomedical domain. A careful analysis of the reason for this gap in performance is carried out, showing that the tokenizers’ vocabulary has a great impact on the aggregation of predictions both at the word level and the word group level. This highlights the effectiveness of using domain specific models for tasks very specific to a concrete linguistic domain. Official test results show a very good performance on the NER task, where all the submissions made are clearly above the average results. However, results for tasks 2 and 3 are very poor, which indicates that a deeper understanding of the underlying nature of those tasks is needed.
Regularization and tempering for a moment‐matching localized particle filter

Iterative ensemble filters and smoothers are now commonly used for geophysical models. Some of these methods rely on a factorization of the observation likelihood function to sample from a posterior density through a set of “tempered” transitions to ensemble members. For Gaussian‐based data assimilation methods, tangent linear versions of nonlinear operators can be relinearized between iterations, thus leading to a solution that is less biased than a single‐step approach. This study adopts similar iterative strategies for a localized particle filter (PF) that relies on the estimation of moments to adjust unobserved variables based on importance weights. This approach builds off a “regularization” of the local PF, which forces weights to be more uniform through heuristic means. The regularization then leads to an adaptive tempering, which can also be combined with filter updates from parametric methods, such as ensemble Kalman filters. The role of iterations is analyzed by deriving the localized posterior probability density assumed by current local PF formulations and then examining how single‐step and tempered PFs sample from this density. From experiments performed with a low‐dimensional nonlinear system, the iterative and hybrid strategies show the largest benefits in observation‐sparse regimes, where only a few particles contain high likelihoods and prior errors are non‐Gaussian. This regime mimics specific applications in numerical weather prediction, where small ensemble sizes, unresolved model error, and highly nonlinear dynamics lead to prior uncertainty that is larger than measurement uncertainty.
Well-founded Semantics for Recursive SHACL

W3C has recently introduced SHACL as a new standard for integrity constraints on RDF graphs. Unfortunately, the standard defines the semantics of non-recursive constraints only, which has spurred recent research efforts into finding a suitable, mathematically crisp semantics for constraints with cyclic dependencies. To this end, Corman et al. [1] introduced a semantics related to supported models known in logic programming, while Andreşel et al.[2] presented a semantics based on stable models known in Answer Set Programming (ASP) . In this paper, we argue that recursive SHACL can be naturally equipped with a semantics inspired in the well-founded semantics for (recursive) DATALOG queries with default negation. This semantics is not only intuitive, but it is also computationally tractable, unlike the previous proposals. To draw a connection with the classic definition of well-founded semantics in logic programming, we provide a simple translation of recursive SHACL under the well-founded semantics into propositional logic programs under the well-founded semantics. This translation is not only of theoretical interest: It can be efficiently implemented using the functionality of standard RDF triple stores (specifically, by issuing a small number of SPARQL queries), followed by an efficient evaluation using an existing engine for computing the well-founded model of a propositional logic program.
POLYNOMIAL CHAOS EXPANSION FOR GENERAL MULTIVARIATE DISTRIBUTIONS WITH CORRELATED VARIABLES

Recently, the use of Polynomial Chaos Expansion (PCE) has been increasing to study the uncertainty in mathematical models for a wide range of applications and several extensions of the original PCE technique have been developed to deal with some of its limitations. But as of to date PCE methods still have the restriction that the random variables have to be statistically independent. This paper presents a method to construct a basis of the probability space of orthogonal polynomials for general multivariate distributions with correlations between the random input variables. We show that, as for the current PCE methods, the statistics like mean, variance and Sobol' indices can be obtained at no significant extra postprocessing costs. We study the behavior of the proposed method for a range of correlation coefficients for an ODE with model parameters that follow a bivariate normal distribution. In all cases the convergence rate of the proposed method is analogous to that for the independent case. Finally, we show, for a canonical enzymatic reaction, how to propagate experimental errors through the process of fitting parameters to a probabilistic distribution of the quantities of interest, and we demonstrate the significant difference in the results assuming independence or full correlation compared to taking into account the true correlation.
TeamUFPR at ABSAPT 2022: Aspect Extraction with CRF and BERT

This paper describes the participation of the TeamUFPR at the Aspect-Based Sentiment Analysis in Portuguese (ABSAPT 2022), framed within the Iberian Languages Evaluation Forum (IberLEF 2022). Two tasks were proposed, aiming at evaluating aspect and sentiment. The first task consists of Aspect Term Extraction, where a set of aspects must be extracted from a group of texts. The second task focuses on Sentiment Orientation Extraction, where sentiment classification must be performed based on three classes. We considered the experiments carried out in the previous event to improve the strategies used for sentiment classification, and we explored an algorithm already discussed in the literature for aspect extraction. Our proposal focused on using the Conditional Random Fields (CRF) algorithm for Aspect identification and Bidirectional Encoder Representations from Transformers (BERT) for sentiment extraction.
Memotion 2: Dataset on Sentiment and Emotion Analysis of Memes (short paper)

Memes are commonly used in social media platforms for humour. Generally, memes consist of an image and embedded text. Memes can be used to spread hate or misinformation, hence it is important to study them. The Memotion task [1] conducted at SemEval 2020, released a data of 10k memes annotated with sentiment label (task A), emotion label (task B) and emotion intensity label (task C). It received ≈ 30 run submissions and 27 papers. However, the best f1 scores were only 0.35, 0.51 and 0.32 respectively for task A, task B, and task C, which shows the need for more extensive research on this topic. In this paper, we release a new dataset, Memotion 2 which has 10k annotated memes along the same directions as Memotion 1.0 This paper detailed baseline system on the Memotion 2.0 data.