Reviewerly/exHarmony huggingface dataset:
Reassessing gApp: Does MWE Discontinuity Always Pose a Challenge to Neural Machine Translation?

In this paper we present research results with gApp, a text-preprocessing system designed for automatically detecting and converting discontinuous multiword expressions (MWEs) into their continuous forms so as to improve the performance of current neural machine translation systems (NMT) (see Hidalgo-Ternero 2021; Hidalgo-Ternero and Corpas Pastor 2020, 2022a, 2022b and 2022c, among others). To test its effectiveness, an experiment with the NMT systems of Google Translate and DeepL has been carried out in the ES>EN/ZH directionalities for the translation of somatisms, i. e., MWEs containing lexemes referring to human or animal body parts (Mellado Blanco 2004). More specifically, we have analysed "Verb Noun Idiomatic Constructions" (VNICs), such as tocar los cojones, tocar los huevos, tocar las narices, and tocar las pelotas. In this regard, some of the unexpected results yielded by the study of these multiword expressions will question the widely accepted conception of phraseological discontinuity as an unequivocal synonym of worse NMT performance.
BERT and Word Embedding for Interest Mining of Instagram Users

With more than one billion monthly active users and nearly 100 million photos shared on the platform daily, Instagram has become among the richest sources of information for detecting users’ interests and trends. However, research works on this social network are limited compared to its competitors, e.g., Facebook and Twitter. There is no doubt that the lack of a publicly labeled dataset that summarizes the content of Instagram profiles is a prime problem bothering the researchers. To overcome this issue, here, for the first time, we present an annotated multidomain interests dataset to train and test OSNs’ users and the methodology to create this dataset from Instagram profiles. In addition, through this work, we propose an automatic detection and classification of Instagram users’ interests. We rely on word embedding representations of words and deep learning techniques to introduce two approaches: (i) a feature-based method and (ii) fine-tuning the BERT model. We observed that BERT fine-tuning performed much better.
Collinear Data Structures and Interaction Models

Models of interactions between multiple fetaures (genes) can be obtained from collinear data sets consisting of multivariate feature vectors. Such models can be designed by minimizing the collinearity criterion function with the basis echange algorithm. The collinearity criterion functions are defined on learning data subsets representing selected categories (e.g. diseases). Based on the minimization of the collinearity functions, interaction models specific to each category can be defined.
Simulation System for Producing Real World Dataset to Predict the Covid-19 Contamination Process⋆

The graph data modeling is the most convenient process to work with in order to answer almost all questions that came across our minds when it comes to covid-19 infection (when was I infected? where? and who gave it me ?). Most of us wonder when we enter a closed space if we are going to face someone who tested positive, but is there any way to predict that? Luckily yes! This could be done by streaming contact data of citizens to a server that looks for any contacts between infected people and non infected ones and sends back a notification for the purpose. The collection, management and exploitation of data in the field of health continues to require the attention of researchers in relation to confidentiality, security, privacy and protection of personal data. Research is active in this area and we are willing to use simulation technologies in order to anticipate the contamination area and predict the propagation due to lack of shared data for reasons listed above. These data flows also constitute a considerable input for the extraction of knowledge and the application of artificial intelligence algorithms and more precisely of machine learning to predict and prevent the evolution of the epidemic on the affected persons as well as its geographical spread, after correlating sensitive data securely from all users, the initial objective is to model the system in the form of a graph and apply graph flow algorithms to predict the propagation network and notify the users if contaminated.
Non-Contextual vs Contextual Word Embeddings in Multiword Expressions Detection

Multiword Expression (MWE) detection is a crucial problem for many NLP applications. Recent methods approach it as a sequence labeling task and require manually annotated corpus. Traditional methods are based on statistical association measures and express limited accuracy, especially on smaller corpora. In this paper, we propose a novel weakly supervised method for extracting MWEs which concentrates on differences between interactions with context between the whole MWE and its component words. The interactions are represented by contextual embeddings (neural language models) and the observations are collected from various occurrence contexts of both the whole MWEs and their single word components. Our method uses a MWE lexicon as the sole knowledge base, and extracts training samples by matching the lexicon against a corpus to build classifiers for MWE recognition by Machine Learning. Thus, our approach does not require a corpus annotated with MWE occurrences, and also works with a limited corpus and a MWE list ( $$\approx $$ 1400 MWEs in this work). It uses a general contextual embeddings model, HerBERTa, a kind of BERT model for Polish. The proposed method was evaluated on the Polish part of the PARSEME corpus and expressed very significant gain in comparison to the top methods from the PARSEME competition. The proposed method can be quite easily applied to other languages.
Defining and Extracting Singleton Design Pattern Information from Object-Oriented Software Program

In software engineering (SE), improving the quality of code and design by relying on pre-established restructuring (refactoring), such as detection and injection of a design pattern are still challenging problems. In this article, we focus on the Singleton design pattern, in which we indicate its variants implementation and define 33 features that can identify this pattern in its standard and non-standard form. Significant information can be extracted by applying the structural and semantic analysis of the source code. So use this information; it becomes easier to identify a pattern and inject it. We created specific data using 20,000 code snippets. This data is used to train deep learning models called RNN-LSTM classifiers to extract information from object-oriented software systems. The empirical result proves that our proposed LSTM-RNN Classifier can successfully extract proposed information with excellent results in terms of prediction recall and F1-score.
Deep Learning-Based Text Recognition of Agricultural Regulatory Document

In this study, an OCR system based on deep learning techniques was deployed to digitize scanned agricultural regulatory documents comprising of certificates and labels. Recognition of the certificates and labels is challenging as they are scanned images of the hard copy form and the layout and size of the text as well as the languages vary between the various countries (due to diverse regulatory requirements). We evaluated and compared between various state-of-the-art deep learning-based text detection and recognition model as well as a packaged OCR library - Tesseract. We then adopted a two-stage approach comprising of text detection using Character Region Awareness For Text (CRAFT) followed by recognition using OCR branch of a multi-lingual text recognition algorithm E2E-MLT. A sliding windows text matcher is used to enhance the extraction of the required information such as trade names, active ingredients and crops. Initial evaluation revealed that the system performs well with a high accuracy of 91.9% for the recognition of trade names in certificates and labels and the system is currently deployed for use in Philippines, one of our collaborator’s sites.
RingNet: Geometric Deep Representation Learning for 3D Multi-domain Protein Shape Retrieval

Many applications use three-dimensional polygon meshes as geometric representation to achieve central tasks, such as 3D object retrieval and classification. However, implementing a deep learning approach dedicated to 3D meshes is a bit hard due to the complexity and irregularity of the mesh surface representation. In this paper, we propose a new geometric deep learning approach dedicated to representation learning, which applies convolutional operations on 3D meshes. In particular, we introduce a ring-unit convolutional operator that aggregates two graphs deduced from the mesh surface. Our network can learn highly discriminating features by avoiding complexity and irregularity problems. We experimentally validated our approach on 3D shape classification tasks and the multi-domain protein shape retrieval challenge. A comparison with the state-of-the-art approaches proved the relevance of the learned features to the accuracy of 3D object classification and retrieval.
Recognizing Handwritten Text Lines in Ancient Document Images Based on a Gated Residual Recurrent Neural Network

Over several decades, many archives and libraries have highlighted the growing need to assist them in the preservation and enrichment of the huge mass of digitized documentary heritage by using efficient handwritten text recognition (HTR) frameworks. To address this issue, we propose in this paper a deep learning based framework for recognizing handwritten text lines in historical document images. The proposed framework is based on a gated residual recurrent neural network, called G2R2N. G2R2N is composed of two modules: encoder and decoder. The encoder module is based on merging the gated and skip connection layers, while the decoder module is composed of the bidirectional long short-term memory (BLSTM), followed by the connectionist temporal classification (CTC) architectures. The proposed framework is evaluated using the same evaluation metrics computed in the context of the ICDAR2017 competition. Numerical and qualitative observations are reported on different benchmark datasets used in the most well-known HTR contests.
Data Augmentation for Morphological Analysis of Histopathological Images Using Deep Learning

In this study, we introduce a data augmentation procedure for histopathology image classification. This is an extension to our previous research, in which we showed the possibility to apply deep learning for morphological analysis of tumour cells. The research problem considered, aimed to distinguish how many cells are located in a structure composed of overlapping cells. We proved that the calculation of the tumour cell number is possible with convolutional neural networks. In this research, we examined the possibility to generate synthetic training data set and to use it for the same purpose. The lack of large data sets is a critical problem in medical image classification and classical augmentation procedures are not sufficient. Therefore, we introduce completely new augmentation approach for histopathology images and we prove the possibility to apply it for a cell-counting problem.
Beyond the Visible Spectrum: Is Person Identity Well Preserved in Thermal Cameras?

Face recognition is a well investigated problem in the computer vision community. Beyond the visible spectrum, it is identified as an active-oriented and attractive research area. In particular, thermal cameras have recently emerged as increasingly important sensors for visual surveillance applications. In addition to the fact that these cameras operate well in challenging environments such as adverse weather and lighting conditions, they are commonly known as keystone biometric solution that preserves person identity. In this paper, we intend to prove that faces could be highly recognized from thermal cameras using a powerful generative adversarial model. This model is employed to deal with the domain shift between thermal and visible sensors. Extensive experiments of different generative models and face recognition systems demonstrate the effectiveness of the proposed pipeline to reveal the person identity even though it is acquired by different sensing modalities, with significant facial variations.
An End to End Bilingual TTS System for Fongbe and Yoruba

This paper aims to present an end to end bilingual TTS system for Yoruba and Fongbe based on Fastspeech 2, a non-autoregressive model. From this baseline, a simple concatenation of speaker, language and phoneme embeddings was used as input for the encoder and the decoder. The training was done on a multi-speaker dataset collected for both languages. Two types of input were used: a shared representation of phoneme between both languages and a language specific representation of phonemes. Then some experimentations were made to test both input representations showing that results are smoother for the shared representation of phoneme. But with all input sets, the proposed model was able to synthesize speech in each language with voice cloning ability. The model produces good speech quality waveform with great fidelity and naturalness and shows its ability to generate speech waveforms for both languages. A comparison was also made between the proposed bilingual system and the same model trained on monolingual dataset to show that the bilingual dataset allows more accurate result.
A New Approach of Morphological Analysis of Arabic Syntagmatic Units Based on a Linguistic Ontology

For a natural language, morphology is the basic layer over which the higher syntactic and semantic layers are built. Several works relating the Arabic language morphology have been proposed to produce a practical morphological analyzer. However, the latter doesn’t consider the words-agglutination in the same syntagmatic unit. This paper attempts to remedy these constraints using the richness of the diacritized Arabic language and its derivational morphology. So, we introduce a morphological analysis method of Arabic syntagmatic units based on a linguistic ontology. The output system named Aramo offers the user the possibility of management of the ontology and the morphological analysis of the syntagmatic units. The evaluation results are encouraging compared to other systems.
Table Recognition in Scanned Documents

Invoices are so vastly used in business. For each invoice, an employee has to verify carefully written data including date, legal, and the courtesy amount present in each table. However, this task is not only time-consuming but also prone to inaccuracies and errors, especially when it comes to processing a massive amount of invoices. A smart capture system is required to facilitate processing invoices automatically and it is more challenging since relevant data are not narrative but arranged in tables. Although it is true that OCR (Optical Character Recognition) is able to read and capture data, it suffers from inefficiency in table locating and loses structural features of tabular data. Table recognition is widely carried out using deep learning and heuristics and a better result was reached as humans would. In this paper, we present a part of a smart capture system for invoices which is based on table recognition workflow for scanned invoices. This workflow consists of three main steps: the first step is a prepossessing step which is used to enhance the quality of scanned invoices. The second step is a deep learning-based table detection approach where we use DocCutout and DocCutmix for data augmentation. The third step is a heuristic-based table structure recognition approach. The presented approaches are evaluated on public data sets.
Multi-Wiki90k: Multilingual Benchmark Dataset for Paragraph Segmentation

In this paper, we present paragraph segmentation using cross-lingual knowledge transfer models. In our solution, we investigate the quality of multilingual models, such as mBERT and XLM-RoBERTa, as well as language independent models, LASER and LaBSE. We study the quality of segmentation in 9 different European languages, both for each language separately and for all languages simultaneously. We offer high quality solutions while maintaining language independence. To achieve our goals, we introduced a new multilingual benchmark dataset called Multi-Wiki90k.
Distributed Anomalies Detection Using Isolation Forest and Spark

Anomaly detection is a major issue for several applications such as industrial failure detection, cybersecurity or transport. Several approaches, such as statistical methods, machine learning and sketch, have been explored by different research communities to detect anomalies in an increasingly challenging context. Indeed, facing the huge volume of data generated at an increasingly fast speed, the response time of the algorithms and their distributivity have become determining criteria, in addition to their accuracy in detecting anomalies. We focus in this paper on the unsupervised anomaly detection algorithm based on binary trees: Isolation Forest. It is a very powerful algorithm with an excellent accuracy and a very low execution time thanks to its linear complexity. In particular, we study the architecture of two distribution solutions of Isolation Forest based on the Apache Spark framework. We then compare the performance of these two solutions by testing them against 4 real commonly used datasets.
Patch Selection for Melanoma Classification

In medical image processing, the most important information is often located on small parts of the image. Patch-based approaches aim at using only the most relevant parts of the image. Finding ways to automatically select the patches is a challenge. In this paper, we investigate two criteria to choose patches: entropy and a spectral similarity criterion. We perform experiments at different levels of patch size. We train a Convolutional Neural Network on the subsets of patches and analyze the training time. We find that, in addition to requiring less preprocessing time, the classifiers trained on the datasets of patches selected based on entropy converge faster than on those selected based on the spectral similarity criterion and, furthermore, lead to higher accuracy. Moreover, patches of high entropy lead to faster convergence and better accuracy than patches of low entropy.
Inferring Event Causality in Films via Common Knowledge Corpora

Human understanding of a movie plot is partially driven by our ability to reason about the causal relations between events. Thus, recognizing causal chains of events is a key requirement for computational models of movie understanding. In this paper, we propose to use available corpora of common-sense knowledge about human behavior for automatically inferring event causality in movie scenes. Our initial experiments with a dataset of annotated movie events and a corpus of human commonsense reasoning demonstrate that a) for 86% of movie events, there exist relevant commonsense rules and those rules can be used for predicting other movie events. b) in 70% of the cases, the consequences of the rules triggered by movie events can accurately or semi-accurately predict subsequent movie events. These preliminary results indicate the potential of automated commonsense reasoning to detect the narrative structure in movies. Hence, the proposed method can contribute to the development of story-related video analytics tools, such as automatic video summarization and movie editing systems.
Reinforcement of BERT with Dependency-Parsing Based Attention Mask

Dot-Product based attention mechanism is among recent attention mechanisms. It showed an outstanding performance with BERT. In this paper, we propose a dependency-parsing mask to reinforce the padding mask, at the multi-head attention units. Padding mask, is already used to filter padding positions. The proposed mask, aims to improve BERT attention filter. The conducted experiments, show that BERT performs better with the proposed mask.
RVT-Transformer: Residual Attention in Answerability Prediction on Visual Question Answering for Blind People

Answerability Prediction on Visual Question Answering is an attractive and novel multi-modal task that can be regarded as a fundamental filter to eliminate the low-qualified samples in practical systems. Instead of focusing on the similarity between images and texts, the critical concern in this task is to accentuate the conflict in visual and textual information. However, the fusion function of the multi-modal system unwittingly decreases the original features of image and text that are essential in answerability prediction. Therefore, inspired by the VT-Transformer, we propose a Residual VT-Transformer model utilizing a residual attention module to combine the single and multiple modality features of images and texts in an aggregated representation. Our architecture allows us not only to maintain the residual signals but also to emphasize the attentive features of vision and text. Through detailed experiments and ablation studies, our model achieves promising results against the competitive baselines in VizWiz-VQA 2020 dataset for blind people.
UML Profile for IoT-Based Applications

In the last few years, the Internet of Things (IoT) has emerged as a new paradigm aimed at providing technological solutions for the monitoring and control of physical entities (so-called objects - cars, furniture, buildings). These new solutions encompass heterogeneous devices capable of: capturing information about the physical entities to which they are attached and/or the environment in which they are inserted, performing sensing tasks; acting on the physical domain, performing actuation tasks; and communicating with each other and/or with other systems via the Internet to achieve common goals. However, Designing systems of this kind is a real challenge and modeling with UML is consolidating itself as a resource to surmount this challenge. To deal with this situation, we contribute by proposing an adaptation of UML2.5 to IoT systems. It is in this context that we have defined a UML2.5 profile for the IoT system. This profile includes a set of stereotypes applied to meta-classes from the UML2.5 meta-model. These stereotypes are complemented by formal constraints in OCL. These extensions allow to improve the consistency checking of reusable system architectures.
Fundamental Formal Language

The article presents a proposal of the formal symbolic notation operate for basic mathematical concepts. This notation is distinguished by its unambiguousness in terms of key terms used in computer science. This notation defines many concepts such as entity, type, structure definition, compositional relationship and aggregation relationship, references, features related to the admission of variation, ordering, uniqueness, generalisation-specialisation relationship, inheritance, and polymorphism mechanism. Purely mathematical notation operates on concepts that are blurry in the sense of computer science. Moreover, in mathematics, there are no direct mechanisms such as inheritance or polymorphism. They can be defined mathematically in many ways, which can sometimes make it difficult to understand such formalisms in the context of computer science solutions. The proposed solution is a response to a research problem defined in this way. The article also presents a case study illustrating how to apply the proposed method.