Search results for "Machine"
showing 10 items of 2592 documents
On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming
2021
Grammar-guided Genetic Programming is a common approach for program synthesis where the user’s intent is given by a set of input/output examples. For use in real-world software development, the generated programs must work on previously unseen test cases too. Therefore, we study in this work the generalizability of programs synthesized by grammar-guided GP with lexicase selection. As benchmark, we analyze proportionate and tournament selection too. We find that especially for program synthesis problems with a low output cardinality (e.g., a Boolean output) lexicase selection overfits the training cases and does not generalize well to unseen test cases. An analysis using common software metr…
A Support Vector Machine Signal Estimation Framework
2018
Support vector machine (SVM) were originally conceived as efficient methods for pattern recognition and classification, and the SVR was subsequently proposed as the SVM implementation for regression and function approximation. Nowadays, the SVR and other kernel‐based regression methods have become a mature and recognized tool in digital signal processing (DSP). This chapter starts to pave the way to treat all the problems within the field of kernel machines, and presents the fundamentals for a simple, framework for tackling estimation problems in DSP using support vector machine SVM. It outlines the particular models and approximations defined within the framework. The chapter concludes wit…
Learning Bayesian Metanetworks from Data with Multilevel Uncertainty
2006
Managing knowledge by maintaining it according to dynamic context is among the basic abilities of a knowledge-based system. The two main challenges in managing context in Bayesian networks are the introduction of contextual (in)dependence and Bayesian multinets. We are presenting one possible implementation of a context sensitive Bayesian multinet-the Bayesian Metanetwork, which implies that interoperability between component Bayesian networks (valid in different contexts) can be also modelled by another Bayesian network. The general concepts and two kinds of such Metanetwork models are considered. The main focus of this paper is learning procedure for Bayesian Metanetworks.
Location-Aware Mobile Intrusion Detection with Enhanced Privacy in a 5G Context
2010
Published version of an article from the journal: Wireless Personal Communications. The original publication is available at Spingerlink. http://dx.doi.org/10.1007/s11277-010-0069-6 The paper proposes a location-aware mobile Intrusion Prevention System (mIPS) architecture with enhanced privacy that is integrated in Managed Security Service (MSS). The solution is envisaged in a future fifth generation telecommunications (5G) context with increased but varying bandwidth, a virtualised execution environment and infrastructure that allows threads, processes, virtual machines and storage to be migrated to cloud computing services on demand, to dynamically scale performance and save power. 5G mob…
On the Characterization of Distributed Virtual Environment Systems
2003
Distributed Virtual Environment systems have experienced a spectacular growth last years. One of the key issues in the design of scalable and cost-effective DVE systems is the partitioning problem. This problem consists of efficiently assigning clients (3-D avatars) to the servers in the system, and some techniques have been already proposed for solving it.
A novel method for network intrusion detection based on nonlinear SNE and SVM
2017
In the case of network intrusion detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal intrusion detection system (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem of this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimise the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE-based oversampling of minority classes. In order to alleviate the issue arising from the curse…
Hybrid descriptive-inferential method for key feature selection in prostate cancer radiomics
2021
In healthcare industry 4.0, a big role is played by radiomics. Radiomics concerns the extraction and analysis of quantitative information not visible to the naked eye, even by expert operators, from biomedical images. Radiomics involves the management of digital images as data matrices, with the aim of extracting a number of morphological and predictive variables, named features, using automatic or semi-automatic methods. Multidisciplinary methods as machine learning and deep learning are fully involved in this field. However, the large number of features requires efficient and effective core methods for their selection, in order to avoid bias or misinterpretations problems. In this work, t…
How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data
2020
This study examines how quickly we can predict users’ ratings on visual aesthetics in terms of simplicity, diversity, colorfulness, craftsmanship. To predict users’ ratings, first we capture gaze behavior while looking at high, neutral, and low visually appealing websites, followed by a survey regarding user perceptions on visual aesthetics towards the same websites. We conduct an experiment with 23 experienced users in online shopping, capture gaze behavior and through employing machine learning we examine how fast we can accurately predict their ratings. The findings show that after 25 s we can predict ratings with an error rate ranging from 9% to 11% depending on which facet of visual ae…
Automated quality control of next generation sequencing data using machine learning
2019
AbstractControlling quality of next generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterized common NGS quality features and developed a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal data and external disease diagnostic datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at the following …
Using System Dynamics to Model Student Performance in an Intelligent Tutoring System
2017
One basic adaptation function of an Intelligent Tutoring System (ITS) consists of selecting the most appropriate next task to be offered to the learner. This decision can be based on estimates, such as the expected performance of the student, or the probability that the student successfully solves each particular task. However, the computation of these values is intrinsically difficult, as they may depend on other complex latent variables that also need to be estimated from observable quantities, e.g. the current student's ability. In this work, we have used system dynamics to model learning and predict the student's performance in a given exercise, in an existing ITS that was developed to …