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2011 16th International Conference on Intelligent System Applications to Power systems, ISAP 2011

作者： Pérez, Rubén F. Pérez, Óscar De La Villa, Antonio Cruz, Pedro León, Gonzalo Martinez-Tomas, Rafael Department of Science and Earth Observation Processing Systems GMV Aerospace and Defence Tres Cantos 28054 Madrid Spain Artificial Intelligence Dpt. Escuela Técnica Superior de Informática - UNED Madrid Spain Department of Electrical Engineering University of Sevilla Spain Department of Artificial Intelligence Universidad Nacional de Educación a Distancia Madrid Spain

Since the research in Information and Communications Technologies has provided strong progress in the field of distributed sensor networks and geo-referenced systems, the fundamental sensor architectures and frameworks developed in Europe are reviewed in this work in order to look for their suitability to Smart Grids, providing with results of interest, drawbacks, and a proposal of general requirements for the identification of an appropriate integration framework. Additionally, a practical case of electrical network is analyzed (IEEE 14-Node Network) oriented to the estimation of fault sensors, describing aspects often studied separately in literature like sensor networks and electrical networks. In this sense, several scenarios have been considered including a simulated processing in order to reveal some of the potential advantages that can be found with the implementation of Semantic Services. © 2011 IEEE.

关键词： Sensor networks

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A Survey of Recent Developments in Testability, Safety and Security of RISC-V Processors

A Survey of Recent Developments in Testability, Safety and S...

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IEEE European Test Symposium (ETS)

作者： Jens Anders Pablo Andreu Bernd Becker Steffen Becker Riccardo Cantoro Nikolaos I. Deligiannis Nourhan Elhamawy Tobias Faller Carles Hernandez Nele Mentens Mahnaz Namazi Rizi Ilia Polian Abolfazl Sajadi Mathias Sauer Denis Schwachhofer Matteo Sonza Reorda Todor Stefanov Ilya Tuzov Stefan Wagner Nuša Zidarič Institute of Smart Sensors University of Stuttgart Stuttgart Germany Universitat Politècnica de València Spain Dpt. of Computer Science University of Freiburg Freiburg Germany Institute of Software Engineering University of Stuttgart Stuttgart Germany Dpt. of Control and Computer Engineering Politecnico di Torino Turin Italy Institute of Computer Architecture and Computer Engineering University of Stuttgart Stuttgart Germany LIACS Leiden University Leiden Netherlands Advantest Europe Boeblingen Germany

With the continued success of the open RISC-V architecture, practical deployment of RISC-V processors necessitates an in-depth consideration of their testability, safety and security aspects. This survey provides an overview of recent developments in this quickly-evolving field. We start with discussing the application of state-of-the-art functional and system-level test solutions to RISC-V processors. Then, we discuss the use of RISC-V processors for safety-related applications; to this end, we outline the essential techniques necessary to obtain safety both in the functional and in the timing domain and review recent processor designs with safety features. Finally, we survey the different aspects of security with respect to RISC-V implementations and discuss the relationship between cryptographic protocols and primitives on the one hand and the RISC-V processor architecture and hardware implementation on the other. We also comment on the role of a RISC-V processor for system security and its resilience against side-channel attacks.

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CLASSIFICATION OF MULTIPLE ANNOTATOR DATA USING VARIATIONAL GAUSSIAN PROCESS INFERENCE

CLASSIFICATION OF MULTIPLE ANNOTATOR DATA USING VARIATIONAL ...

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European Signal Processing Conference

作者： Emre Besler Pablo Ruiz Rafael Molina Aggelos K. Katsaggelos Dpt. of Electrical Engineering and Computer Science. Northwestern University Dpto. de Ciencias de la Computación e I.A. Universidad de Granada

ISBN: (纸本)9781509018918

In this paper we address supervised learning problems where, instead of having a single annotator who provides the ground truth, multiple annotators, usually with varying degrees of expertise, provide conflicting labels for the same sample. Once Gaussian Process classification has been adapted to this problem we propose and describe how Variational Bayes inference can be used to, given the observed labels, approximate the posterior distribution of the latent classifier and also estimate each annotator's reliability. In the experimental section, we evaluate the proposed method on both generated synthetic and real data, and compare it with state of the art crowdsourcing methods.

关键词： Crowdsourcing Gaussian processes Solid modeling Europe Signal processing Supervised learning Bayes methods Semi-supervised Learning entity modeling Gaussian processes Bayes methods signal processing Crowdsource detection zone Gauss

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Scheduling jobs and preventive maintenance activities on parallel machines

Scheduling jobs and preventive maintenance activities on par...

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作者： Rebai, Maher Kacem, Imed Adjallah, Kondo H. University of Technology of Troyes Department of Industrial Systems 12 rue Marie Curie 10000 Troyes France University Paul Verlaine Metz Department of Informatique Ile du Saulcy 57000 Metz France Ecole Nationale d'Ingnieurs de Metz Electrical and Computer Engineering Dpt. Ile du Saulcy 57045 Metz France

ISBN: (纸本)9789604742318

We propose in this study a hierarchical method for the problem of scheduling N jobs on M parallel machines where each machine should be maintained once during the planning horizon. The maintenance tasks should be continuously executed because the maintenance resources are not sufficient. Our objective is to find a schedule composed of the jobs and the maintenance tasks in which the total sum of the job's weighted completion times and the preventive maintenance cost are simultaneously minimized. The proposed hierarchical method is essentially based on a linear model and an Evolutionary algorithm. Computational experiments are performed on randomly generated instances. The results show that the proposed method is able to produce appropriate solutions for the problem.

关键词： Evolutionary algorithms

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Electromagnetic Analysis of Nanoscale Heterogeneity - The Domain-Integrated Perspective

Electromagnetic Analysis of Nanoscale Heterogeneity - The Do...

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European Conference on Microwave

作者： Ioan E. Lager Guy A. E. Vandenbosch Martin Štumpf Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology the Netherlands Electrical Engineering Dpt. Katholieke Universiteit Leuven Belgium SIX Research Centre Brno University of Technology the Czech Republic

This paper introduces a new paradigm in the electromagnetic (EM) analysis of largely inhomogeneous nanostructures. It is shown that the high degree of inhomogeneity may render the traditional discretisation of such topologies problematic. A new discretisation scheme that is much better matched to these topologies is proposed. The scheme involves a more adequate meshing and discretisation formalism, in conjunction with an original combination of dual space-time EM field quantities to be calculated. The pivotal field equations are elaborately discussed, with an emphasis on their computational implications.

关键词： Nonhomogeneous media Magnetics Face Integral equations Nanoscale devices Mathematical model Finite element analysis

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Learning System Based on Contextual Awareness for Clinical Practice in Nursing Courses

Learning System Based on Contextual Awareness for Clinical P...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Jorge Gómez Gómez J.F. Huete Velssy Hernandez Riaño Department of Systems Engineering - Research Group GNOCIX Universidad del Sinu-Monteria Colombia Dpt. Computer Science and Artificial Intelligence University of Granada-CITIC_UGR Spain Research Group SOCRATES Universidad de Cordoba-Monteria Colombia

The ubiquitous learning is used as a means of supporting teaching processes by means of the use of mobile and wireless communication technologies, sensors and tracking mechanisms / tracking, working together to integrate students with their environment. The process of clinical learning for nursing students may be the main cause of increased stress on teachers and students, given the problematic nature of the patients. This paper presents an ubiquitous learning system based on active learning methodology that provides context awareness support for nursing courses. An experiment with control and experimental groups with nursing students demonstrates that for theoretical concepts to be successfully transferred into practice, the context of practice needs to be considered.

关键词： Medical services Context Education Mobile communication Learning systems Mobile handsets Personal digital assistants

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Variational Gaussian process for sensor fusion

Variational Gaussian process for sensor fusion

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European Signal Processing Conference (EUSIPCO)

作者： Neda Rohani Pablo Ruiz Emre Besler Rafael Molina Aggelos K. Katsaggelos Dpt. of Electrical Engineering and Computer Science Northwestern University USA Dpto. de Ciencias de la Computación e I.A Universidad de Granada Spain

In this paper, we introduce a new Gaussian Process (GP) classification method for multisensory data. The proposed approach can deal with noisy and missing data. It is also capable of estimating the contribution of each sensor towards the classification task. We use Bayesian modeling to build a GP-based classifier which combines the information provided by all sensors and approximates the posterior distribution of the GP using variational Bayesian inference. During its training phase, the algorithm estimates each sensor's weight and then uses this information to assign a label to each new sample. In the experimental section, we evaluate the classiication performance of the proposed method on both synthetic and real data and show its applicability to different scenarios.

关键词： Robot sensing systems Signal processing algorithms Bayes methods Training Europe Signal processing Gaussian processes

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Automated Chinese Language Proficiency Scoring by utilizing Siamese Convolutional Neural Network and fusion based approach

Automated Chinese Language Proficiency Scoring by utilizing ...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Anne Kwong Junaid Hussain Muzamal Patrick Yu Zhang Guimin Lin Dpt. Research and Development Digital Galaxy Technologies Limited Honkkong Guangdong Sheng China Al-Khawarizmi Institute of Computer Science-KICS University of Engineering and Technology Lahore Lahore Pakistan

ISBN: (数字)9781728145815

ISBN: (纸本)9781728145822

The previous approaches have failed to effectually score the language proficiency of a non-native speakers especially in case of non- English languages which are complex and a slight change of pronunciation can alter the nature of the word. In this study, we proposed an automated language scoring system to test the proficiency of Chinese language. We have employed a novel fusion approach of a 38-feature based model and a Siamese convolutional neural network (Siamese CNN) which can accuracy identify the difference between the native speech and the test taker's speech. The results show that out model have achieved comparable performance to the state of the art and solved the pronunciation problems as well. Furthermore, we have provided a fusion based approach and provided extensive amount of experiments which shows that our method is state of the art and can be utilized in real time Chinese language proficiency scoring.

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Understanding the Conceptual Structure of Large Language Models through Bibliographical Network

Understanding the Conceptual Structure of Large Language Mod...

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IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： V. Duarte-Martínez I.J. Perez P. Ducange M.J Cobo Escuela Superior Politécnica del Litoral ESPOL Facultad de Ingeniería en Electricidad y Computación ESPOL Polytechnic University Guayaquil Ecuador Dpt. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain Dpt. of Information Engineering University of Pisa Italy

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Large Language Models represent a transformative technology at the forefront of artificial intelligence and natural language processing, with applications spanning diverse domains. This study conducts a comprehensive science mapping analysis of the LLMs research field, leveraging bibliometric techniques to uncover its thematic structure, trends, and global actors involved. Utilizing data from the Web of science, a corpus of 1303 research documents from 2010 to 2023 is analyzed, revealing a notable surge in research activity, particularly in recent years. Key thematic areas driving research within the field are identified, including chatbot, code generation, augmented reality, transformers, and machine learning paradigms. Foundational technologies such as transformers are pivotal in shaping the research landscape, while emerging themes like prompt learning hint at future directions. This study offers valuable insights for researchers, practitioners, and policymakers seeking to navigate the dynamic landscape of LLMs research and harness its full potential for societal benefit.

关键词： Technological innovation Codes Navigation Collaboration Market research Transformers Chatbots

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Fake news detection in Spanish using deep learning techniques

arXiv

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arXiv 2021年

作者： Martínez-Gallego, Kevin Álvarez-Ortiz, Andrés M. Arias-Londoño, Julián D. Intelligent Information Systems Lab Universidad de Antioquia Calle 67 No. 53 - 108 Medellín050010 Colombia Intelligent Information Systems Lab Dpt. of Systems Engineering and Computer Science Universidad de Antioquia Calle 67 No. 53 - 108 Medellín050010 Colombia

This paper addresses the problem of fake news detection in Spanish using Machine Learning techniques. It is fundamentally the same problem tackled for the English language;however, there is not a significant amount of publicly available and adequately labeled fake news in Spanish to effectively train a Machine Learning model, similarly to those proposed for the English language. Therefore, this work explores different training strategies and architectures to establish a baseline for further research in this area. Four datasets were used, two in English and two in Spanish, and four experimental schemes were tested, including a baseline with classical Machine Learning models, trained and validated using a small dataset in Spanish. The remaining schemes include state-of-the-art Deep Learning models trained (or fine-tuned) and validated in English, trained and validated in Spanish, and fitted in English and validated with automatic translated Spanish sentences. The Deep Learning architectures were built on top of different pre-trained Word Embedding representations, including GloVe, ELMo, BERT, and BETO (a BERT version trained on a large corpus in Spanish). According to the results, the best strategy was a combination of a pre-trained BETO model and a Recurrent Neural Network based on LSTM layers, yielding an accuracy of up to 80%;nonetheless, a baseline model using a Random Forest estimator obtained similar outcomes. Additionally, the translation strategy did not yield acceptable results because of the propagation error;there was also observed a significant difference in models performance when trained in English or Spanish, mainly attributable to the number of samples available for each language. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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