Pub Date : 2022-01-01DOI: 10.5220/0011276500003277
Maja Mitreska, Tashko Pavlov, Kostadin Mishev, M. Simjanoska
: Creating multilingual end-to-end AMR models requires a large amount of cross-lingual data making the parsing and generating tasks exceptionally challenging when dealing with low-resource languages. To avoid this obstacle, this paper presents a cross-lingual AMR (xAMR) pipeline that incorporates the intuitive translation approach to and from the English language as a baseline for further utilization of the AMR parsing and generation models. The proposed pipeline has been evaluated via the cosine similarity of multiple state-of-the-art sentence embeddings used for representing the original and the output sentences generated by our xAMR approach. Also, BLEU and ROUGE scores were used to evaluate the preserved syntax and the word order. xAMR results were compared to multilingual AMR models’ performance for the languages experimented within this research. The results showed that our xAMR outperforms the multilingual approach for all the languages discussed in the paper and can be used as an alternative approach for abstract meaning representation of low-resource languages.
{"title":"xAMR: Cross-lingual AMR End-to-End Pipeline","authors":"Maja Mitreska, Tashko Pavlov, Kostadin Mishev, M. Simjanoska","doi":"10.5220/0011276500003277","DOIUrl":"https://doi.org/10.5220/0011276500003277","url":null,"abstract":": Creating multilingual end-to-end AMR models requires a large amount of cross-lingual data making the parsing and generating tasks exceptionally challenging when dealing with low-resource languages. To avoid this obstacle, this paper presents a cross-lingual AMR (xAMR) pipeline that incorporates the intuitive translation approach to and from the English language as a baseline for further utilization of the AMR parsing and generation models. The proposed pipeline has been evaluated via the cosine similarity of multiple state-of-the-art sentence embeddings used for representing the original and the output sentences generated by our xAMR approach. Also, BLEU and ROUGE scores were used to evaluate the preserved syntax and the word order. xAMR results were compared to multilingual AMR models’ performance for the languages experimented within this research. The results showed that our xAMR outperforms the multilingual approach for all the languages discussed in the paper and can be used as an alternative approach for abstract meaning representation of low-resource languages.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"7 1","pages":"132-139"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78631082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0011266300003277
Florian Kirchbuchner, Moritz Andres, Julian von Wilmsdorff, Arjan Kuijper
: In this paper, we will demonstrate a novel approach using artificial neural networks to enhance signal processing for indoor localization based on electric field measurement systems Up to this point, there exist a variety of approaches to localize persons by using wearables, optical sensors, acoustic methods and by using Smart Floors. All capacitive approaches use, to the best of our knowledge, analytic signal processing techniques to calculate the position of a user. While analytic methods can be more transparent in their functionality, they often come with a variety of drawbacks such as delay times, the inability to compensate defect sensor inputs or missing accuracy. We will demonstrate machine learning approaches especially made for capacitive systems resolving these challenges. To train these models, we propose a data labeling system for person localization and the resulting dataset for the supervised machine learning approaches. Our findings show that the novel approach based on artificial neural networks with a time convolutional neural network (TCNN) architecture reduces the Euclidean error by 40% (34.8cm Euclidean error) in respect to the presented analytical approach (57.3cm Euclidean error). This means a more precise determination of the user position of 22.5cm centimeter on average.
{"title":"Neural Networks for Indoor Localization based on Electric Field Sensing","authors":"Florian Kirchbuchner, Moritz Andres, Julian von Wilmsdorff, Arjan Kuijper","doi":"10.5220/0011266300003277","DOIUrl":"https://doi.org/10.5220/0011266300003277","url":null,"abstract":": In this paper, we will demonstrate a novel approach using artificial neural networks to enhance signal processing for indoor localization based on electric field measurement systems Up to this point, there exist a variety of approaches to localize persons by using wearables, optical sensors, acoustic methods and by using Smart Floors. All capacitive approaches use, to the best of our knowledge, analytic signal processing techniques to calculate the position of a user. While analytic methods can be more transparent in their functionality, they often come with a variety of drawbacks such as delay times, the inability to compensate defect sensor inputs or missing accuracy. We will demonstrate machine learning approaches especially made for capacitive systems resolving these challenges. To train these models, we propose a data labeling system for person localization and the resulting dataset for the supervised machine learning approaches. Our findings show that the novel approach based on artificial neural networks with a time convolutional neural network (TCNN) architecture reduces the Euclidean error by 40% (34.8cm Euclidean error) in respect to the presented analytical approach (57.3cm Euclidean error). This means a more precise determination of the user position of 22.5cm centimeter on average.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"2006 1","pages":"25-33"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86934707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0011320600003277
Marco Parola, Federico A. Galatolo, Matteo Torzoni, M. Cimino, G. Vaglini
: Structural Health Monitoring (SHM) of civil structures using IoT sensors is a major emerging challenge. SHM aims to detect and identify any deviation from a reference condition, typically a damage-free baseline, to keep track of the relevant structural integrity. Machine Learning (ML) techniques have recently been employed to empower vibration-based SHM systems. Supervised ML can provide more information than unsupervised ML, but it requires human intervention to appropriately label data describing the nature of the damage. However, labelled data related to damage conditions of civil structures are often unavailable. To overcome this limitation, a key solution is a Digital Twin relying on physics-based numerical models to simulate the structural response in terms of the vibration recordings provided by IoT devices during the events of interest, such as wind or seismic excitations. This paper presents such comprehensive approach to address the damage localization task by exploiting a Convolutional Neural Network (CNN). Early experimental results related to a pilot application involving a sample structure, show the potential of the proposed approach and the reusability of the trained system in presence of varying loading scenarios.
{"title":"Structural Damage Localization via Deep Learning and IoT Enabled Digital Twin","authors":"Marco Parola, Federico A. Galatolo, Matteo Torzoni, M. Cimino, G. Vaglini","doi":"10.5220/0011320600003277","DOIUrl":"https://doi.org/10.5220/0011320600003277","url":null,"abstract":": Structural Health Monitoring (SHM) of civil structures using IoT sensors is a major emerging challenge. SHM aims to detect and identify any deviation from a reference condition, typically a damage-free baseline, to keep track of the relevant structural integrity. Machine Learning (ML) techniques have recently been employed to empower vibration-based SHM systems. Supervised ML can provide more information than unsupervised ML, but it requires human intervention to appropriately label data describing the nature of the damage. However, labelled data related to damage conditions of civil structures are often unavailable. To overcome this limitation, a key solution is a Digital Twin relying on physics-based numerical models to simulate the structural response in terms of the vibration recordings provided by IoT devices during the events of interest, such as wind or seismic excitations. This paper presents such comprehensive approach to address the damage localization task by exploiting a Convolutional Neural Network (CNN). Early experimental results related to a pilot application involving a sample structure, show the potential of the proposed approach and the reusability of the trained system in presence of varying loading scenarios.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"26 1","pages":"199-206"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87338781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0011300800003277
M. Fernandes, Plinio Moreno
: Generative pre-trained transformers belong to the breakthroughs in Natural Language Processing (NLP), allowing Human-Robot Interactions ( e.g. the creation of an open-domain chatbot). However, a substantial amount of research and available data are in English, causing low-resourced languages to be overlooked. This work addresses this problem for European Portuguese with two options: (i) Translation of the sentences before and after using the model fine-tuned on an English-based dataset, (ii) Translation of the English-based dataset to Portuguese and then fine-tune this model on it. We rely on the DialoGPT (dialogue generative pre-trained transformer), a tunable neural conversational answer generation model that learns the basic skills to conduct a dialogue. We use two sources of evaluation: (i) Metrics for text generation based on uncertainty ( i.e. perplexity), and similarity between sentences ( i.e. BLEU, METEOR and ROUGE) and (ii) Human-based evaluation of the sentences. The translation of sentences before and after of the modified DialoGPT model, using the Daily Dialogue dataset led to the best results.
{"title":"Open-domain Conversational Agent based on Pre-trained Transformers for Human-Robot Interaction","authors":"M. Fernandes, Plinio Moreno","doi":"10.5220/0011300800003277","DOIUrl":"https://doi.org/10.5220/0011300800003277","url":null,"abstract":": Generative pre-trained transformers belong to the breakthroughs in Natural Language Processing (NLP), allowing Human-Robot Interactions ( e.g. the creation of an open-domain chatbot). However, a substantial amount of research and available data are in English, causing low-resourced languages to be overlooked. This work addresses this problem for European Portuguese with two options: (i) Translation of the sentences before and after using the model fine-tuned on an English-based dataset, (ii) Translation of the English-based dataset to Portuguese and then fine-tune this model on it. We rely on the DialoGPT (dialogue generative pre-trained transformer), a tunable neural conversational answer generation model that learns the basic skills to conduct a dialogue. We use two sources of evaluation: (i) Metrics for text generation based on uncertainty ( i.e. perplexity), and similarity between sentences ( i.e. BLEU, METEOR and ROUGE) and (ii) Human-based evaluation of the sentences. The translation of sentences before and after of the modified DialoGPT model, using the Daily Dialogue dataset led to the best results.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"1 1","pages":"168-175"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78457680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0011300300003277
M. Rezaei, Nasseh Tabrizi
: Recommender systems are rapidly becoming an integral part of our daily lives. They play a crucial role in overcoming the overloading problem of information by suggesting and personalizing the recommended items. Collaborative filtering, content-based filtering, and hybrid methods are examples of traditional recommender systems which had been used for straightforward prediction problems. More complex problems can be solved with new methods which are applied to recommender systems, such as reinforcement learning algorithms. Markov decision process and reinforcement learning can take part in solving these problems. Recent developments in applying reinforcement learning methods to recommender systems make it possible to use them in order to solve problems with the massive environment and states. A review of the reinforcement learning recommender system will follow the traditional and reinforcement learning-based methods formulation, their evaluation, challenges, and recommended future work.
{"title":"Recommender System using Reinforcement Learning: A Survey","authors":"M. Rezaei, Nasseh Tabrizi","doi":"10.5220/0011300300003277","DOIUrl":"https://doi.org/10.5220/0011300300003277","url":null,"abstract":": Recommender systems are rapidly becoming an integral part of our daily lives. They play a crucial role in overcoming the overloading problem of information by suggesting and personalizing the recommended items. Collaborative filtering, content-based filtering, and hybrid methods are examples of traditional recommender systems which had been used for straightforward prediction problems. More complex problems can be solved with new methods which are applied to recommender systems, such as reinforcement learning algorithms. Markov decision process and reinforcement learning can take part in solving these problems. Recent developments in applying reinforcement learning methods to recommender systems make it possible to use them in order to solve problems with the massive environment and states. A review of the reinforcement learning recommender system will follow the traditional and reinforcement learning-based methods formulation, their evaluation, challenges, and recommended future work.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"10 1","pages":"148-159"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82352401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-37317-6_1
K. Loumponias, Andreas Kosmatopoulos, T. Tsikrika, S. Vrochidis, Y. Kompatsiaris
{"title":"Modified SkipGram Negative Sampling Model for Faster Convergence of Graph Embedding","authors":"K. Loumponias, Andreas Kosmatopoulos, T. Tsikrika, S. Vrochidis, Y. Kompatsiaris","doi":"10.1007/978-3-031-37317-6_1","DOIUrl":"https://doi.org/10.1007/978-3-031-37317-6_1","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"33 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74920859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0011142600003277
Lerina Aversano, M. Bernardi, Vincenzo Calgano, Marta Cimitile, Concetta Esposito, Martina Iammarino, M. Pisco, S. Spaziani, Chiara Verdone
{"title":"Using Machine Learning for Classification of Cancer Cells from Raman Spectroscopy","authors":"Lerina Aversano, M. Bernardi, Vincenzo Calgano, Marta Cimitile, Concetta Esposito, Martina Iammarino, M. Pisco, S. Spaziani, Chiara Verdone","doi":"10.5220/0011142600003277","DOIUrl":"https://doi.org/10.5220/0011142600003277","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"13 44 1","pages":"15-24"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87225251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0011276000003277
Konstantin Bogdanoski, Kostadin Mishev, D. Trajanov
{"title":"Blanket Clusterer: A Tool for Automating the Clustering in Unsupervised Learning","authors":"Konstantin Bogdanoski, Kostadin Mishev, D. Trajanov","doi":"10.5220/0011276000003277","DOIUrl":"https://doi.org/10.5220/0011276000003277","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"18 1","pages":"125-131"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87230740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0011301700003277
Dhvani Katkoria, Jaya Sreevalsan-Nair
: Road surface geometry provides information about navigable space in autonomous driving. Ground plane estimation is done on “road” points after semantic segmentation of three-dimensional (3D) automotive LiDAR point clouds as a precursor to this geometry extraction. However, the actual geometry extraction is less explored, as it is expensive to use all “road” points for mesh generation. Thus, we propose a coarser surface approximation using road edge points. The geometry extraction for the entire sequence of a trajectory provides the complete road geometry, from the point of view of the ego-vehicle. Thus, we propose an automated system, RoSELS (Road Surface Extraction for LiDAR point cloud Sequence). Our novel approach involves ground point detection and road geometry classification, i.e. frame classification , for determining the road edge points. We use appropriate supervised and pre-trained transfer learning models, along with computational geometry algorithms to implement the workflow. Our results on SemanticKITTI show that our extracted road surface for the sequence is qualitatively and quantitatively close to the reference trajectory.
{"title":"RoSELS: Road Surface Extraction for 3D Automotive LiDAR Point Cloud Sequence","authors":"Dhvani Katkoria, Jaya Sreevalsan-Nair","doi":"10.5220/0011301700003277","DOIUrl":"https://doi.org/10.5220/0011301700003277","url":null,"abstract":": Road surface geometry provides information about navigable space in autonomous driving. Ground plane estimation is done on “road” points after semantic segmentation of three-dimensional (3D) automotive LiDAR point clouds as a precursor to this geometry extraction. However, the actual geometry extraction is less explored, as it is expensive to use all “road” points for mesh generation. Thus, we propose a coarser surface approximation using road edge points. The geometry extraction for the entire sequence of a trajectory provides the complete road geometry, from the point of view of the ego-vehicle. Thus, we propose an automated system, RoSELS (Road Surface Extraction for LiDAR point cloud Sequence). Our novel approach involves ground point detection and road geometry classification, i.e. frame classification , for determining the road edge points. We use appropriate supervised and pre-trained transfer learning models, along with computational geometry algorithms to implement the workflow. Our results on SemanticKITTI show that our extracted road surface for the sequence is qualitatively and quantitatively close to the reference trajectory.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"11 1","pages":"55-67"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84890944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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