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IEEE International Conference on Image Processing

作者： Juan G. Serra Pablo Ruiz Rafael Molina Aggelos K. Katsaggelos Dpto. de Ciencia de la Computacion e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science Northwestern University

ISBN: (纸本)9781467399623

In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm is validated on both synthetic and real experiments and compared with other state-of-the-art methods, such as Support Vector Machine and Gaussian Processes, demonstrating its improved performance.

关键词： Image Classification Bayes Methods Inference Algorithms Image classification Bayes methods Inference algorithms Variational Inference MULTISPECTRAL Taxonomy Gaussian processes regression function images Support Vector Network

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Maximin Fairness-profit Tradeoff in Project Budget Allocation

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Procedia computer science 2016年 100卷 313-320页

作者： Maurizio Naldi Gaia Nicosia Andrea Pacifici Ulrich Pferschy Dpt. of Computer Science and Civil Engineering University of Rome Tor Vergata Rome Italy Dipartimento di Ingegneria Universita’ Roma Tre Rome Italy Department of Statistics and Operations Research University of Graz Graz Austria

Companies typically select those projects that maximize their profit as the primary criterion, within the limited budget at their disposal. This criterion may lead to some company departments getting an exceedingly large share of the overall budget and induce a negative perception of unfairness among the less favourite ones. We investigate how profit optimization can be sought after while achieving the desired level of fairness at the same time. Adopting a maximin approach to fairness and using an Integer Linear Programming solver, we show that a linear trade-off is possible, since fairness and profit exhibit a nearly perfect linear anticorrelation. Fairness can be improved by even a relatively small reduction of profit, especially in large companies (i.e., managing a large number of projects).

关键词： Budget allocation Multi-project management Project Portfolio Fairness

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Variational Gaussian process for missing label crowdsourcing classification problems

Variational Gaussian process for missing label crowdsourcing...

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IEEE Workshop on Machine Learning for Signal Processing

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

ISBN: (纸本)9781509007479

In this paper we address the crowdsourcing problem, where a classifier must be trained without knowing the real labels. For each sample, labels (which may not be the same) are provided by different annotators (usually with different degrees of expertise). The problem is formulated using Bayesian modeling, and considers scenarios where each annotator may label a subset of the training set samples only. Although Bayesian approaches have been previously proposed in the literature, we introduce Variational Bayes inference to develop an iterative algorithm where all latent variables are automatically estimated. In the experimental section the proposed model is evaluated and compared with other state-of-the-art methods on two real datasets.

关键词： Crowdsourcing Bayes methods Reliability Probabilistic logic Training Gaussian processes Noise measurement

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LEARNING FILTERS IN GAUSSIAN PROCESS CLASSIFICATION PROBLEMS

LEARNING FILTERS IN GAUSSIAN PROCESS CLASSIFICATION PROBLEMS

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IEEE International Conference on Image Processing

作者： Pablo Ruiz Javier Mateos Rafael Molina Aggelos K. Katsaggelos Dpto. de Ciencia de la Computacion e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science. Northwestern University

ISBN: (纸本)9781479957521

Many real classification tasks are oriented to sequence (neighbor) labeling, that is, assigning a label to every sample of a signal while taking into account the sequentiality (or neighborhood) of the samples. This is normally approached by first filtering the data and then performing classification. In consequence, both processes are optimized separately, with no guarantee of global optimality. In this work we utilize Bayesian modeling and inference to jointly learn a classifier and estimate an optimal filterbank. Variational Bayesian inference is used to approximate the posterior distributions of all unknowns, resulting in an iterative procedure to estimate the classifier parameters and the filterbank coefficients. In the experimental section we show, using synthetic and real data, that the proposed method compares favorably with other classification/filtering approaches, without the need of parameter tuning.

关键词： Gaussian Process classification Filter estimation Analysis representation

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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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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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Analysis and Detection of Melanoma through Collective Intelligence with AI

Analysis and Detection of Melanoma through Collective Intell...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Enrique Fernández-Morales Carlos L. Sanchez-Bocanegra Rafael Pastor-Vargas José-Juan Pereyra-Rodriguez Juan M. Haut José Alberto Benítez-Andrades Communications and Control Systems Dpt. Computer Science Faculty UNED Madrid Spain Communications and Control Systems Dpt. Computer Science Faculty UNED Malaga Spain Virgen Del Rocio Hospital Universitario Seville Spain School of Tecnology University of Extremadura Cáceres Spain Dept. of Electric Systems and Automatics Engineering Universidad de León León Spain

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.

关键词： Accuracy Melanoma Collective intelligence Image capture Transformers Solids Artificial intelligence

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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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