242 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    Validation and Verification of Safety-Critical Systems in Avionics

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    This research addresses the issues of safety-critical systems verification and validation. Safety-critical systems such as avionics systems are complex embedded systems. They are composed of several hardware and software components whose integration requires verification and testing in compliance with the Radio Technical Commission for Aeronautics standards and their supplements (RTCA DO-178C). Avionics software requires certification before its deployment into an aircraft system, and testing is mandatory for certification. Until now, the avionics industry has relied on expensive manual testing. The industry is searching for better (quicker and less costly) solutions. This research investigates formal verification and automatic test case generation approaches to enhance the quality of avionics software systems, ensure their conformity to the standard, and to provide artifacts that support their certification. The contributions of this thesis are in model-based automatic test case generations approaches that satisfy MC/DC criterion, and bidirectional requirement traceability between low-level requirements (LLRs) and test cases. In the first contribution, we integrate model-based verification of properties and automatic test case generation in a single framework. The system is modeled as an extended finite state machine model (EFSM) that supports both the verification of properties and automatic test case generation. The EFSM models the control and dataflow aspects of the system. For verification, we model the system and some properties and ensure that properties are correctly propagated to the implementation via mandatory testing. For testing, we extended an existing test case generation approach with MC/DC criterion to satisfy RTCA DO-178C requirements. Both local test cases for each component and global test cases for their integration are generated. The second contribution is a model checking-based approach for automatic test case generation. In the third contribution, we developed an EFSM-based approach that uses constraints solving to handle test case feasibility and addresses bidirectional requirements traceability between LLRs and test cases. Traceability elements are determined at a low-level of granularity, and then identified, linked to their source artifact, created, stored, and retrieved for several purposes. Requirements’ traceability has been extensively studied but not at the proposed low-level of granularity

    Adaptive Automated Machine Learning

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    The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open. In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions: 1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue. 2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures. 3. We introduce an online AutoML framework that allows the incremental adaptation of ML models. We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances

    A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

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    The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

    Applications of graph theory to wireless networks and opinion analysis

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    La teoría de grafos es una rama importante dentro de la matemática discreta. Su uso ha aumentado recientemente dada la conveniencia de los grafos para estructurar datos, para analizarlos y para generarlos a través de modelos. El objetivo de esta tesis es aplicar teoría de grafos a la optimización de redes inalámbricas y al análisis de opinión. El primer conjunto de contribuciones de esta tesis versa sobre la aplicación de teoría de grafos a redes inalámbricas. El rendimiento de estas redes depende de la correcta distribución de canales de frecuencia en un espacio compartido. Para optimizar estas redes se proponen diferentes técnicas, desde la aplicación de heurísticas como simulated annealing a la negociación automática. Cualquiera de estas técnicas requiere un modelo teórico de la red inalámbrica en cuestión. Nuestro modelo de redes Wi-Fi utiliza grafos geométricos para este propósito. Los vértices representan los dispositivos de la red, sean clientes o puntos de acceso, mientras que las aristas representan las señales entre dichos dispositivos. Estos grafos son de tipo geométrico, por lo que los vértices tienen posición en el espacio, y las aristas tienen longitud. Con esta estructura y la aplicación de un modelo de propagación y de uso, podemos simular redes inalámbricas y contribuir a su optimización. Usando dicho modelo basado en grafos, hemos estudiado el efecto de la interferencia cocanal en redes Wi-Fi 4 y mostramos una mejora de rendimiento asociada a la técnica de channel bonding cuando se usa en regiones donde hay por lo menos 13 canales disponibles. Por otra parte, en esta tesis doctoral hemos aplicado teoría de grafos al análisis de opinión dentro de la línea de investigación de SensoGraph, un método con el que se realiza un análisis de opinión sobre un conjunto de elementos usando grafos de proximidad, lo que permite manejar grandes conjuntos de datos. Además, hemos desarrollado un método de análisis de opinión que emplea la asignación manual de aristas y distancias en un grafo para estudiar la similaridad entre las muestras dos a dos. Adicionalmente, se han explorado otros temas sin relación con los grafos, pero que entran dentro de la aplicación de las matemáticas a un problema de la ingeniería telemática. Se ha desarrollado un sistema de votación electrónica basado en mixnets, secreto compartido de Shamir y cuerpos finitos. Dicha propuesta ofrece un sistema de verificación numérico novedoso a la vez que mantiene las propiedades esenciales de los sistemas de votación

    Sustainable Value Co-Creation in Welfare Service Ecosystems : Transforming temporary collaboration projects into permanent resource integration

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    The aim of this paper is to discuss the unexploited forces of user-orientation and shared responsibility to promote sustainable value co-creation during service innovation projects in welfare service ecosystems. The framework is based on the theoretical field of public service logic (PSL) and our thesis is that service innovation seriously requires a user-oriented approach, and that such an approach enables resource integration based on the service-user’s needs and lifeworld. In our findings, we identify prerequisites and opportunities of collaborative service innovation projects in order to transform these projects into sustainable resource integration once they have ended

    Recuperación de polifenoles de efluentes de almazara mediante procesos de membrana y tratamiento biológico de las corrientes de rechazo

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    Tesis por compendio[ES] Toneladas de aceite de oliva son producidas cada año en el área mediterránea, generando aguas residuales con elevada carga orgánica (COD) y polifenoles (TPhs). Los TPhs son compuestos fitotóxicos, sin embargo, poseen una alta actividad antioxidante, siendo valiosos para su comercialización. La Tesis Doctoral pretende implementar la economía circular para el tratamiento de estas aguas residuales. Para ello, varias combinaciones de procesos fueron estudiados, para recuperar TPhs y reincorporar estas aguas en el proceso productivo. El agua estudiada corresponde a agua de lavado de aceite de oliva (OOWW, "olive oil washing wastewater"), obtenida a la salida de la centrifugación vertical (lavado del aceite), generada en la elaboración de aceite de oliva mediante centrifugación de dos fases. El estudio contempla la utilización de procesos de membrana, resinas de adsorción y tratamiento biológico. Primero se realizó un pretratamiento (flotación, sedimentación y filtración con cartucho) eliminando 89% de grasas y aceites y 40% de color, turbidez y sólidos en suspensión. Luego fue alimentada al proceso de Ultrafiltración (UF) para obtener un permeado rico en TPhs con baja COD. Diferentes membranas, condiciones operacionales (presión transmembranal (TMP) y velocidad tangencial (CFV)) y protocolos de limpieza fueron estudiados. Modelos matemáticos semi-empíricos, método de superficies de respuesta (RSM) y redes neuronales artificiales (ANN) fueron utilizados para predecir el comportamiento de la densidad de flujo de permeado y analizar el tipo de ensuciamiento predominante. La membrana UP005 a TMP de 2 bar y CFV de 2.5m/s fue seleccionada, con una densidad de flujo de permeado estable de 40L/h·m2, bajo rechazo de TPhs (8%) y alto rechazo de COD (61%). Los modelos matemáticos indicaron que más de un proceso de ensuciamiento contribuyeron al ensuciamiento de las membranas. El análisis estadístico ANOVA de RSM mostró que la CFV como la TMP afectan a la densidad de flujo de permeado. Mediante ANN fue posible predecir los datos experimentales de variación de densidad de flujo de permeado con el tiempo. La nanofiltración (NF) y la ósmosis directa (FO) se estudiaron para concentrar los TPhs presentes en el permeado de UF. En la NF se analizaron varias membranas bajo diferentes condiciones operacionales para obtener el mayor rechazo de TPhs. La membrana NF270 a CFV de 1m/s y TMP de 10 bar, logró una densidad de flujo de permeado estable de 74L/h·m2, rechazo de TPhs del 94% y rechazo de COD del 83%. Para el estudio del ensuciamiento de las membranas se utilizaron dos técnicas espectroscópicas, fluorescencia 2D y FTIR, obteniendo información sobre la adsorción de algunos compuestos sobre la superficie de las membranas, y evaluar la eficiencia del protocolo de limpieza. En la FO dos membranas fueron analizadas para la concentración de TPhs. También se estudió el uso de aguas residuales procedentes de la etapa de fermentación en la elaboración de aceitunas de mesa (FTOP) como disolución de arrastre debido a su alta salinidad. Con la membrana HFFO6 (caudal de 30 L/h) se logró la concentración de TPhs en un 79% y la dilución de la FTOP. Cuatro resinas de adsorción fueron estudiadas para recuperar los TPhs presentes en los concentrados de la FO y de la NF. Se estudiaron diferentes concentraciones de resina, tiempos de contacto y disolventes de desorción para la obtención de un concentrado puro, rico en TPhs. Los mejores resultados se obtuvieron con 40 g/L de resina MN200 y una disolución 50% etanol/agua como disolvente. Finalmente, las aguas resultantes (concentrado de FO y rechazos de NF y UF) fueron sometidas a tratamientos biológicos. Primero se realizaron estudios para evaluar la concentración inicial de los reactores biológicos. Mediante tratamiento biológico SBR se logró eliminar en gran medida la COD y los TPhs (rechazo de UF) presentes, logrando obtener efluentes con características aptas para ser utilizadas como agua de limpieza de maquinaria.[CA] Tones d'oli d'oliva són produïdes cada any a l'àrea mediterrània, generant aigües residuals amb càrrega orgànica elevada (COD) i polifenols (TPhs). Els TPhs són compostos fitotòxics, no obstant això, tenen una alta activitat antioxidant, sent valuosos per a la seva comercialització. La Tesi Doctoral pretén implementar l¿economia circular per al tractament d¿aquestes aigües residuals. Per això, diverses combinacions de processos van ser estudiats, per recuperar TPhs i reincorporar aquestes aigües al procés productiu. L'aigua estudiada correspon a aigua de rentat d'oli d'oliva (OOWW, olive oil washing wastewater), obtinguda a la sortida de la centrifugació vertical (rentat de l'oli), generada en l'elaboració d'oli d'oliva mitjançant centrifugació de dues fases. L'estudi contempla la utilització de processos de membrana, resines d'adsorció i tractament biològic. Primer es va realitzar un pretractament (flotació, sedimentació i filtració amb cartutx) eliminant 89% de greixos i olis i 40% de color, terbolesa i sòlids en suspensió. Després va ser alimentada al procés d'Ultrafiltració (UF) per obtenir un permeat ric en TPhs amb baixa COD. Diferents membranes, condicions operacionals (pressió transmembranal (TMP) i velocitat tangencial (CFV)) i protocols de neteja van ser estudiats. Models matemàtics semi-empírics, mètode de superfícies de resposta (RSM) i xarxes neuronals artificials (ANN) van ser utilitzats per predir el comportament de la densitat de flux de permeat i analitzar el tipus d'embrutament predominant. La membrana UP005 a TMP de 2 bar i CFV de 2.5m/s va ser seleccionada, amb una densitat de flux de permeat estable de 40L/h·m2, baix rebuig de TPhs (8%) i alt rebuig de COD (61%) . Els models matemàtics van indicar que més d'un procés d'embrutament van contribuir a embrutar les membranes. L'anàlisi estadística ANOVA de RSM va mostrar que la CFV com la TMP afecten la densitat de flux de permeat. Mitjançant ANN va ser possible predir les dades experimentals de variació de densitat de flux de permeat amb el temps. La nanofiltració (NF) i l'osmosi directa (FO) es van estudiar per concentrar els TPhs presents al permeat d'UF. A la NF es van analitzar diverses membranes sota diferents condicions operacionals per obtenir el major rebuig de TPhs. La membrana NF270 a CFV de 1m/s i TMP de 10 bar, va aconseguir una densitat de flux de permeat estable de 74L/h·m2, rebuig de TPhs del 94% i rebuig de COD del 83%. Per estudiar l'embrutament de les membranes es van utilitzar dues tècniques espectroscòpiques, fluorescència 2D i FTIR, obtenint informació sobre l'adsorció d'alguns compostos sobre la superfície de les membranes, i avaluar l'eficiència del protocol de neteja. A la FO dues membranes van ser analitzades per a la concentració de TPhs. També es va estudiar l'ús d'aigües residuals procedents de l'etapa de fermentació en l'elaboració d'olives de taula (FTOP) com a dissolució d'arrossegament per la seva alta salinitat. Amb la membrana HFFO6 (cabal de 30 L/h) es va aconseguir la concentració de TPhs en un 79% i la dilució de la FTOP. Quatre resines d'adsorció van ser estudiades per recuperar els TPhs presents als concentrats de la FO i de la NF. Es van estudiar diferents concentracions de resina, temps de contacte i dissolvents de desorció per obtenir un concentrat pur, ric en TPhs. Els millors resultats es van obtenir amb 40 g/L de resina MN200 i una dissolució 50% etanol/aigua com a dissolvent. Finalment, les aigües resultants (concentrat de FO i rebutjos de NF i UF) van ser sotmeses a tractaments biològics. Primer es van fer estudis per avaluar la concentració inicial dels reactors biològics. Mitjançant tractament biològic SBR es va aconseguir eliminar en gran mesura la COD i els TPhs (rebuig d'UF) presents, aconseguint obtenir efluents amb característiques aptes per ser utilitzades com a aigua de neteja de maquinària.[EN] Tons of olive oil are produced each year in the Mediterranean area, generating wastewater with a high organic load (COD) and polyphenols (TPhs). TPhs are phytotoxic compounds, however, they have a high antioxidant activity, being valuable for their commercialization. The Doctoral Thesis aims to implement the circular economy for the treatment of these wastewaters. For this, various combinations of processes were studied to recover TPhs and reincorporate these waters into the production process. The water studied corresponds to olive oil washing water (OOWW), obtained at the outlet of the vertical centrifugation (oil washing), generated in the production of olive oil by means of two-phase centrifugation. The study contemplates the use of membrane processes, adsorption resins and biological treatment. First, a pretreatment (flotation, sedimentation and cartridge filtration) was carried out, eliminating 89% of fats and oils and 40% of colour, turbidity and suspended solids. Then it was fed to the Ultrafiltration (UF) process to obtain a permeate rich in TPhs with low COD. Different membranes, operational conditions (transmembrane pressure (TMP) and cross low velocity (CFV)) and cleaning protocols were studied. Semi-empirical mathematical models, the response surface method (RSM) and artificial neural networks (ANN) were used to predict the behavior of the permeate flux density and to analyze the predominant type of fouling. The UP005 membrane at 2 bar TMP and 2.5m/s CFV was selected, with a stable permeate flux density of 40L/h·m2, low TPhs rejection (8%) and high COD rejection (61%). Mathematical models indicated that more than one fouling process contributed to the fouling of the membranes. Statistical analysis ANOVA of RSM showed that both CFV and TMP affect permeate flux density. Through ANN it was possible to predict the experimental data of permeate flux density variation over time. Nanofiltration (NF) and forward osmosis (FO) were studied to concentrate the TPhs present in the UF permeate. In the NF several membranes were analyzed under different operational conditions to obtain the highest rejection of TPhs. The NF270 membrane at CFV of 1m/s and TMP of 10 bar, achieved a stable permeate flux density of 74L/h·m2, TPhs rejection of 94% and COD rejection of 83%. To study the fouling of the membranes, two spectroscopic techniques were used, 2D fluorescence and FTIR, obtaining information on the adsorption of some compounds on the surface of the membranes, and evaluating the efficiency of the cleaning protocol. In the FO two membranes were analyzed for the concentration of TPhs. The use of wastewater from the fermentation stage in the production of table olives (FTOP) as a stripping solution due to its high salinity was also studied. With the HFFO6 membrane (flow rate of 30 L/h) the concentration of TPhs was achieved by 79% and the dilution of the FTOP. Four adsorption resins were studied to recover the TPhs present in the FO and NF concentrates. Different resin concentrations, contact times and desorption solvents were studied to obtain a pure concentrate, rich in TPhs. The best results were obtained with 40 g/L of MN200 resin and a 50% ethanol/water solution as solvent. Finally, the resulting waters (FO concentrate and NF and UF rejections) were subjected to biological treatments. First, studies were carried out to evaluate the initial concentration of the biological reactors. Using SBR biological treatment, it was possible to largely eliminate the COD and the TPhs (rejection of UF) present, managing to obtain effluents with suitable characteristics to be used as machinery cleaning water.The authors acknowledge the financial support from the Spanish Ministry of Economy, Industry and Competitiveness through the project CTM2017-88645-R and The European Union through the Operational Program of the Social Fund (FSE) of the Comunitat Valenciana 2014-2020, ACIF-2018 and BEFPI-2021, and the Associate Laboratory for Green Chemistry-LAQV which is financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020).Cifuentes Cabezas, MS. (2022). Recuperación de polifenoles de efluentes de almazara mediante procesos de membrana y tratamiento biológico de las corrientes de rechazo [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191508Compendi

    The automatic processing of multiword expressions in Irish

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    It is well-documented that Multiword Expressions (MWEs) pose a unique challenge to a variety of NLP tasks such as machine translation, parsing, information retrieval, and more. For low-resource languages such as Irish, these challenges can be exacerbated by the scarcity of data, and a lack of research in this topic. In order to improve handling of MWEs in various NLP tasks for Irish, this thesis will address both the lack of resources specifically targeting MWEs in Irish, and examine how these resources can be applied to said NLP tasks. We report on the creation and analysis of a number of lexical resources as part of this PhD research. Ilfhocail, a lexicon of Irish MWEs, is created through extract- ing MWEs from other lexical resources such as dictionaries. A corpus annotated with verbal MWEs in Irish is created for the inclusion of Irish in the PARSEME Shared Task 1.2. Additionally, MWEs were tagged in a bilingual EN-GA corpus for inclusion in experiments in machine translation. For the purposes of annotation, a categorisation scheme for nine categories of MWEs in Irish is created, based on combining linguistic analysis on these types of constructions and cross-lingual frameworks for defining MWEs. A case study in applying MWEs to NLP tasks is undertaken, with the exploration of incorporating MWE information while training Neural Machine Translation systems. Finally, the topic of automatic identification of Irish MWEs is explored, documenting the training of a system capable of automatically identifying Irish MWEs from a variety of categories, and the challenges associated with developing such a system. This research contributes towards a greater understanding of Irish MWEs and their applications in NLP, and provides a foundation for future work in exploring other methods for the automatic discovery and identification of Irish MWEs, and further developing the MWE resources described above
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