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检索条件"机构=Dpt. of Computer Science and Biomedical Informatics"

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Intelligent and semantic real-time process of the Greek LOD for enhancing citizen awareness in public expenditures

Intelligent and semantic real-time process of the Greek LOD ...

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13th International Conference on Web Information Systems Engineering, WISE 2012

作者： Vafopoulos, M. Meimaris, M. Papantoniou, A. Anagnostopoulos, I. Xidias, I. Alexiou, G. Vafeiadis, G. Loumos, V. School of Electrical and Computer Engineering National Technical University of Athens Greece Computer Science and Biomedical Informatics Dpt. University of Central Greece Greece

ISBN: (纸本)9783642350627

The provision of publicly available Open Data targets to provide transparency in several public sector decisions and actions. However, this information is served massively and in different forms - mostly due to different bureaucratic procedures- while its diffusion does not occur at regular or at least generally predictable time intervals. Thus, even though the information is available by the involved public sectors and enterprises. citizens are overwhelmed from the size/inconsistency of the information they deal with. In this paper, we present a publicly accessible Web point (***/), which aims to promote transparency and enhance citizen awareness regarding public spending in Greece. The information is based on intelligent and semantic real-time processed open data that are derived from the open API of "Diavgeia" (Clarity) Program (***/en/) and TAXIS - Taxation Information System (***/), while it is served through easily consumed visualization charts and diagrams. © 2012 Springer-Verlag.

关键词： Transparency

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Evaluating multiple LVCSR model combination in NTCIR-3 speech-driven web retrieval task 8

Evaluating multiple LVCSR model combination in NTCIR-3 speec...

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8th European Conference on Speech Communication and Technology, EUROSPEECH 2003

作者： Matsushita, Masahiko Nishizaki, Hiromitsu Utsuro, Takehito Kodama, Yasuhiro Nakagawa, Seiichi Dpt. Information Computer Sciences Toyohashi University of Technology Japan Dpt. Intelligence Science Technology Graduate School of Informatics Kyoto University Japan

This paper studies speech-driven Web retrieval models which accepts spoken search topics (queries) in the NTCIR-3 Web retrieval task. The major focus of this paper is on improving speech recognition accuracy of spoken queries then improving retrieval accuracy in speech-driven Web retrieval. We experimentally evaluate the techniques of combining outputs of multiple LVCSR models in recognition of spoken queries. As model combination techniques, we compare the SVM learning technique conventional voting schemes such as ROVER. We show that the techniques of multiple LVCSR model combination can achieve improvement both in speech recognition and retrieval accuracies in speech-driven text retrieval. We also show that model combination by SVM learning outperforms conventional voting schemes both in speech recognition retrieval accuracies.

关键词： Speech recognition

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Data driven constraints for the SVM

Data driven constraints for the SVM

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3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Darkner, Sune Clemmensen, Line H. Univrsity of Copenhagen Dpt. of Computer Science Denmark Technical University of Denmark DTU Informatics Denmark

ISBN: (纸本)9783642354274

We propose a generalized data driven constraint for support vector machines exemplified by classification of paired observations in general and specifically on the human ear canal. This is particularly interesting in dynamic cases such as tissue movement or pathologies developing over time. Assuming that two observations of the same subject in different states span a vector, we hypothesise that such structure of the data contains implicit information which can aid the classification, thus the name data driven constraints. We derive a constraint based on the data which allow for the use of the 1- norm on the constraint while still allowing for the application of kernels. We specialize the proposed constraint to orthogonality of the vectors between paired observations and the estimated hyperplane. We show that imposing the constraint of orthogonality on the paired data yields a more robust classifier solution, compared to the SVM i.e. reduces variance and improves classification rates. We present a quantitative measure of the information level contained in the pairing and test the method on simulated as well as a high-dimensional paired data set of ear-canal surfaces. © 2012 Springer-Verlag.

关键词： Classification (of information)

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IxaMed at CLEF eHealth 2018 task 1: ICD10 coding with a sequence-to-sequence approach 19

IxaMed at CLEF eHealth 2018 task 1: ICD10 coding with a sequ...

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19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018

作者： Atutxa, A. Casillas, A. Ezeiza, N. Fresno, V. Goenaga, I. Gojenola, K. Martínez, R. Oronoz, M. Perez-De-Viñaspre, O. Dpt. Languages and Computer Systems School of Engineering UPV EHU Bilbao Spain Dpt. Electricity and Electronics Fac. of Science and Technology UPV EHU Leioa Spain Dpt. Languages and Computer Systems Faculty of Informatics UPV EHU Donostia-San Sebastian United States Dpt. Languages and Computer Systems School of Computer Engineering UNED Madrid Spain

Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve a large-scale classification problem, as there are thousands of possible codes. In this working notes paper, we present our system and the results of the CLEF 2018 eHealth Evaluation Task 1 on Multilingual Information Extraction-ICD10 coding. This benchmark addresses information extraction in written text with focus on several languages, specifically Hungarian, Italian and French. The goal is to automatically assign ICD10 codes to diagnostic terms of death certificates. The problem can be cast in different ways, for example as a multilabel classification task or as sequence-to-sequence prediction. Our proposal follows this last approach, with promising results, well above the average results for the task. It only relies on the material provided by the task organizers, allowing the application of the same system to all datasets.

关键词： Machine learning

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Real-life paradigms of wireless network security attacks

Real-life paradigms of wireless network security attacks

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15th Panhellenic Conference on informatics, PCI 2011

作者： Mavridis, I.P. Androulakis, A.-I.E. Halkias, A.B. Mylonas, Ph. Dpt. of Computer Science and Biomedical Informatics University of Central Greece Lamia Greece Image Video and Multimedia Laboratory National Technical University of Athens Athens Greece

ISBN: (纸本)9780769543895

Wirelesses Local Area Networks (WLANs) have become more prevalent and are widely deployed in many popular places like university campuses, cafés, airports, residences, etc. However, WLAN security is a very important but usually neglected issue. Focusing on three major types of typical wireless security standards: WEP, WPA and WPA2, we aim to explore the current state-of-the-art in security protocols and to present an overview of their real-life vulnerabilities by issuing successful attacks against WEP and WPA-protected WLANs. © 2011 IEEE.

关键词： Network security

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A Review of Homomorphic Encryption and its Contribution to the Sector of Health Services 23

A Review of Homomorphic Encryption and its Contribution to t...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Fotios Roumpies Athanasios Kakarountas Dpt. of Computer Science and Biomedical Informatics University of Thessaly Greece

ISBN: (纸本)9798400716263

Homomorphic encryption is a groundbreaking cryptographic method that has made giant contributions to healthcare by addressing the urgent need for steady and privacy-keeping information analysis and sharing. This encryption approach permits information to be processed while nonetheless in its encrypted form, permitting healthcare businesses to perform complex computations on confidential patient information without compromising character privacy or data protection. It paved the way for secure cloud-based facts storage, sharing, and collaborative healthcare research, facilitating advancements in fact-driven selection-making, customized medicinal drugs, and remote affected person tracking. Homomorphic encryption has emerged as a vital enabler of innovation by maintaining the confidentiality of affected personal information while enabling meaningful analysis.

关键词： Healthcare Homomorphic Encryption data security digital libraries secure data sharing

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A Reward Modulated Spiked Timing Depended Plasticity inspired algorithm applied on a MultiLayer Perceptron 23

A Reward Modulated Spiked Timing Depended Plasticity inspire...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Georgios Giannakas Maria Sapounaki Vasileios Tsoukas Athanasios Kakarountas Dpt. of Computer Science and Biomedical Informatics University of Thessaly Greece

ISBN: (纸本)9798400716263

The creation of a framework in which traditional Machine Learning and neuromorphic algorithms compete to solve a shared Reinforcement Learning environment is presented in this work. In addition, this configuration allows the exploitation of modern and widely-used Machine Learning libraries. The PyTorch framework is used to investigate the expanded capabilities and potential of training an action-critic network pair comprised of specialised units using a custom learning algorithm. The policy and value networks utilised in this context are fully interconnected MultiLayer Perceptrons. The training procedure employs two distinct algorithms: an algorithm inspired by Reward Modulated Spiked Timing Dependent Plasticity and the conventional Back Propagation technique. A comparative evaluation and analysis of the findings is performed.

关键词： Agent Bio & Nature Inspired Computing Machine Learning PyTorch Reinforcement Learning Reward Modulated Spiked Timing Depended Plasticity autograd

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An ontology-based framework for building economic evaluation models in health care

An ontology-based framework for building economic evaluation...

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13th International Congress on Medical informatics Europe, MIE 1996

作者： Quaglini, S. Dazzi, L. Falasconi, S. Marchetti, M. Stefanelli, M. Barosi, G. Dpt. Computer Science and Systems University of Pavia Italy Lab. of Medical Informatics IRCCS PoliclinicoS Matten Pavia Italy

ISBN: (纸本)9051992785

This paper describes a framework which provides health care professionals with a set of tools for building decision-theory based support systems for resource management optimization of health care policies in selected areas. The generated models share a common ontology underlying the different types of represented knowledge needed to tackle budget-related decisions. Each chunk of knowledge is maintained by a specific professional, i.e. a physician, a hospital administrator, an economist, etc. Thus the resulting systems will facilitate the communication among them, allowing the physician to weigh his own opinions with constraints outlined by the administrator, and vice-versa. Another advantage is the improvement of result presentation in a standard format, that is one of the most recognized problems in the reusability of cost-effectiveness studies within different settings. The proposed framework is designed to be integrated into a distributed Health Care Information System. © The authors 1996.

关键词： Cost effectiveness

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Semantifying Twitter: The Influence Tracker Ontology

Semantifying Twitter: The Influence Tracker Ontology

引用

International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Gerasimos Razis Ioannis Anagnostopoulos Computer Science and Biomedical Informatics Dpt. University of Thessaly Lamia Greece

In this paper, we propose an ontology schema towards semantification provision of Twitter social analytics. The ontology is deployed over a publicly available service that measures how influential a Twitter account is, by combining its social activity and interaction over Twitter sphere. Apart from influential quantity and quality measures, the service provides a SPARQL endpoint where users can perform advance semantic queries through the RDFized Twitter entities (mentions, replies, hash tags, photos, URLs) over the semantic graph.

关键词： Twitter Measurement Ontologies Resource description framework Semantics Uniform resource locators

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks 21

Leveraging protein-protein interactions in phenotype predict...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Smeriglio, Riccardo Rosell-Mirmi, Joana Radeva, Petia Abante, Jordi Universitat de Barcelona Dpt. of Physics Spain Politecnico di Torino Control and Computer Engineering Dpt. Turin Italy Headache Research Group Vall d'Hebron Institut de Recerca Catalonia Barcelona Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Biomedical Sciences Spain Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain

ISBN: (纸本)9798350356632

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer's disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer's disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases. © 2024 IEEE.

关键词： Prediction models

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