Kernel density estimation based on Ripley’s correction

In this paper, we investigate a technique inspired by Ripley’s circumference method to correct bias of density estimation of edges (or frontiers) of regions. The idea of the method was theoretical and difficult to implement. We provide a simple technique – based of properties of Gaussian kernels – to efficiently compute weights to correct border bias on frontiers of the region of interest, with an automatic selection of an optimal radius for the method. We illustrate the use of that technique to visualize hot spots of car accidents and campsite locations, as well as location of bike thefts.     

Diagnosis of lung nodule using Moran’s index and Geary’s coefficient in computerized tomography images

This paper analyzes the application of Moran’s index and Geary’s coefficient to the characterization of lung nodules as malignant or benign in computerized tomography images. The characterization method is based on a process that verifies which combination of measures, from the proposed measures, has been best able to discriminate between the benign and malignant nodules using stepwise discriminant analysis. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these in predicting the classification for each nodule. In order to verify this application we also describe tests that were carried out using a sample of 36 nodules: 29 benign and 7 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. The two analyzed functions and its combinations have provided above 90% of accuracy and a value area under receiver operation characteristic (ROC) curve above 0.85, that indicates a promising potential to be used as nodules signature measures. The preliminary results of this approach are very encouraging in characterizing nodules using the two functions presented.

Three-qubit Grover’s algorithm using superconducting quantum interference devices in cavity-QED

We present a scheme for the implementation of three-qubit Grover’s algorithm using four-level superconducting quantum interference devices (SQUIDs) coupled to a superconducting resonator. The scheme is based on resonant, off-resonant interaction of the cavity field with SQUIDs and application of classical microwave pulses. We show that adjustment of SQUID level spacings during the gate operations, adiabatic passage, and second-order detuning are not required that leads to faster implementation. We also show that the marked state can be searched with high fidelity even in the presence of unwanted off-resonant interactions, level decay, and cavity dissipation.     

Consent for processing children’s personal data in the EU: following in US footsteps?

With the recent adoption of the General Data Protection Regulation (GDPR), the European Union (EU) assigned a prominent role to parental consent in order to protect the personal data of minors online. For the first time, the GDPR requires parental consent before information society service providers can process the personal data of children under 16 years of age. This provision is new for Europe and faces many interpretation and implementation challenges, but not for the US, which adopted detailed rules for the operators that collect personal information from children under the Children’s Online Privacy Protection Act (COPPA) almost two decades ago. The article critically assesses the provisions of the GDPR related to the consent of minors, and makes a comparative analysis with the requirements stipulated in the COPPA in order to identify pitfalls and lessons to be learnt before the new rules in the EU become applicable.     

Exchange rates determination based on genetic algorithms using Mendel’s principles: Investigation and estimation under uncertainty

A genetic algorithm using Mendel’s principle (Mendel-GA), in which the random assignment of alleles from parents to offsprings is implied by the Mendel genetic operator, is proposed for the exchange rates determination problem. Besides the traditional genetic operators of selection, crossover, and mutation, Mendel’s principles are included, in the form of an operator in the genetic algorithm’s evolution process. In the quantitative analysis of exchange rates determination, the Mendel-GA examines the exchange rate fluctuations at the short-run horizon. Specifically, the aim is to revisit the determination of high-frequency exchange rates and examine the differences between the method of genetic algorithms and that of the traditional estimation methods. A simulation with a given initial conditions has been devised in MATLAB, and it is shown that the Mendel-GA can work valuably as a tool for the exchange rates estimation modelling with high-frequency data.     

Goal-oriented error estimation and h-adaptivity for Maxwell’s equations

Goal-oriented error estimates, based on dual solutions, are derived for the S-parameters of a waveguide cavity resonator and a computationally cheap method is proposed to compute these. Numerical results show that the error estimators are relatively accurate, usually well within a factor 2 from the exact errors. Adaptive mesh-refinement based on these estimates recovers optimal convergence rates for complete and incomplete, first and second order, curl-conforming finite elements of Nédélec type, despite singularities at reentrant corners. It is also shown numerically that the convergence rate is independent of the element-order when singularities are present and the mesh is refined uniformly.     

Small Space Analogues of Valiant’s Classes and the Limitations of Skew Formulas

In the uniform circuit model of computation, the width of a boolean circuit exactly characterizes the “space” complexity of the computed function. Looking for a similar relationship in Valiant’s algebraic model of computation, we propose width of an arithmetic circuit as a possible measure of space. In the uniform setting, we show that our definition coincides with that of VPSPACE at polynomial width. We introduce the class VL as an algebraic variant of deterministic log-space L; VL is a subclass of VP. Further, to define algebraic variants of non-deterministic space-bounded classes, we introduce the notion of “read-once” certificates for arithmetic circuits. We show that polynomial-size algebraic branching programs (an algebraic analog of NL) can be expressed as read-once exponential sums over polynomials in ${{\sf VL}, {\it i.e.}\quad{\sf VBP} \in \Sigma^R \cdot {\sf VL}}$ . Thus, read-once exponential sums can be viewed as a reasonable way of capturing space-bounded non-determinism. We also show that Σ ^R ·VBP = VBP, i.e. VBPs are stable under read-once exponential sums. Though the best upper bound we have for Σ ^R ·VL itself is VNP, we can obtain better upper bounds for width-bounded multiplicatively disjoint (md-) circuits. Without the width restriction, md- arithmetic circuits are known to capture all of VP. We show that read-once exponential sums over md- constant-width arithmetic circuits are within VP and that read-once exponential sums over md- polylog-width arithmetic circuits are within VQP. We also show that exponential sums of a skew formula cannot represent the determinant polynomial.     

A case study in class library verification: Java’s vector class

This paper presents a verification of an invariant property for the Vector class from JAVA’s standard library (API). The property says (essentially) that the actual size of a vector is less than or equal to its capacity. It is shown that this “safety” or “data integrity” property is maintained by all methods of the Vector class, and that it holds for all objects created by the constructors of the Vector class. The verification of the Vector class invariant is done with the proof tool PVS. It is based on a semantics of JAVA in higher order logic. The latter is incorporated in a special purpose compiler, the LOOP tool, which translates JAVA classes into logical theories. It has been applied to the Vector class for this case study. The actual verification takes into account the object-oriented character of JAVA: (non-final) methods may always be overridden, so that one cannot rely on a particular implementation. Instead, one has to reason from method specifications in such cases. This project demonstrates the feasibility of tool-assisted verification of non-trivial properties for non-trivial JAVA classes. Published online: 10 May 2001     

Ball detection in soccer images using isophote’s curvature and discriminative features

Ball recognition in soccer matches is a critical issue for automatic soccer video analysis. Unfortunately, because of the difficulty in solving the problem, many efforts of numerous researchers have still not produced fully satisfactory results in terms of accuracy. This paper proposes a ball recognition approach that introduces a double level of innovation. Firstly, a randomized circle detection approach based on the local curvature information of the isophotes is used to identify the edge pixels belonging to the ball boundaries. Then, ball candidates are validated by a learning framework formulated into a three-layered model based on a variation of the conventional local binary pattern approach. Experimental results were obtained on a significant set of real soccer images, acquired under challenging lighting conditions during Italian “Serie A” matches. The results have been also favorably compared with the leading state-of-the-art methods.     

A novel neuro-estimator and it’s application to parameter estimation in a remotely piloted vehicle

The paper presents a novel neuro-computing approach to the problem of state estimation by means of a hybrid combination of a Hopfield neural network and a feedforward multilayer neural net capable to solve certain optimization problems. This neuro-estimator is very appropriate for the real-time implementation of nonlinear state estimators, especially when the modeling of uncertainty is considered in the problem. The proposed estimator is applied to estimate the aerodynamic parameters of a remotely piloted vehicle. Simulation results show the effectiveness of the proposed method.     

Pose estimation from multiple cameras based on Sylvester’s equation

In this paper, we introduce a method to estimate the object’s pose from multiple cameras. We focus on direct estimation of the 3D object pose from 2D image sequences. Scale-Invariant Feature Transform (SIFT) is used to extract corresponding feature points from adjacent images in the video sequence. We first demonstrate that centralized pose estimation from the collection of corresponding feature points in the 2D images from all cameras can be obtained as a solution to a generalized Sylvester’s equation. We subsequently derive a distributed solution to pose estimation from multiple cameras and show that it is equivalent to the solution of the centralized pose estimation based on Sylvester’s equation. Specifically, we rely on collaboration among the multiple cameras to provide an iterative refinement of the independent solution to pose estimation obtained for each camera based on Sylvester’s equation. The proposed approach to pose estimation from multiple cameras relies on all of the information available from all cameras to obtain an estimate at each camera even when the image features are not visible to some of the cameras. The resulting pose estimation technique is therefore robust to occlusion and sensor errors from specific camera views. Moreover, the proposed approach does not require matching feature points among images from different camera views nor does it demand reconstruction of 3D points. Furthermore, the computational complexity of the proposed solution grows linearly with the number of cameras. Finally, computer simulation experiments demonstrate the accuracy and speed of our approach to pose estimation from multiple cameras.     

Justification for the Use of Cohen’s Kappa Statistic in Experimental Studies of NLP and Text Mining

Modern metrics for evaluating agreement coefficients between the experimental results and expert opinion are compared, and the possibility of using these metrics in experimental research in automatic text processing by machine learning methods is assessed. The choice of Cohen’s kappa coefficient as a measure of expert opinion agreement in the NLP and Text Mining problems is justified. An example of using Cohen’s kappa coefficient for evaluating the level of agreement between the opinion of an expert and the results of ML classification and the measure of agreement of expert opinions in the alignment of sentences of the Kazakh-Russian parallel corpus is given. Based on this analysis, it is proved that Cohen’s kappa coefficient is one of the best statistical methods for determining the level of agreement in experimental studies due to its ease of use, computing simplicity, and high accuracy of the results.

     

Identifying potential areas of understorey coffee in Ethiopia’s highlands using predictive modelling

Coffee production is one of the main economic activities in Ethiopia, representing about 40% of the country’s economy. Coffee is particularly important in the Ethiopian highlands, where appropriate climate allows higher productivity and quality. The Ethiopian highlands also host an outstanding biodiversity, being considered one of the world’s most important biodiversity hotspots. In this context, conciliating agricultural practices with biodiversity conservation is a priority task for researchers and other stakeholders. However, identifying and mapping understorey coffee plantations in Ethiopian highlands is particularly challenging due to the presence of scattered exotic trees and the characteristics of understorey cultivation. In this research, we mapped potential areas of understorey coffee using predictive modelling and evaluated how projected changes in climate would affect the suitability of coffee production in the study area. Landscape maps, which were mapped using remote-sensing data based on object-based image analysis, remotely sensed spectral vegetation indices, and climatic variables were used to delineate probability maps showing the most likely location of understorey coffee. Normalized difference vegetation index and maximum temperature and precipitation were considered the best predictors for explaining the spatial distribution of understorey coffee. The accuracy of the probability map was validated based on existing understorey coffee areas mapped during field surveys. In addition, we show that potential changes in temperature and precipitation by 2050 are likely to shift suitable areas of understorey coffee to higher altitudes, affecting the landscape changes dynamics in the region.     

A psycho-cognitive segmentation of organ donors in Egypt using Kohonen’s self-organizing maps

This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on organ donation in Egypt. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and nonlinear patterns. The results show that major variables affecting organ donation are related to perceived benefits/risks of organ donation, organ donation knowledge, attitudes toward organ donation, and intention to donate organs. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.     

Image segmentation using Atanassov’s intuitionistic fuzzy sets

The problem of segmentation in spite of all the work over the last decades, is still an important research field and also a critical preprocessing step for image processing, mostly due to the fact that finding a global optimal threshold that works well for all kind of images is indeed a very difficult task that, probably, will never be accomplished.During the past years, fuzzy logic theory has been successfully applied to image thresholding. In this paper we describe a thresholding technique using Atanassov’s intuitionistic fuzzy sets (A-IFSs). This approach uses Atanassov’s intuitionistic index values for representing the hesitance of the expert in determining whether the pixel belongs to the background or that it belongs to the object. First, we describe the general framework of this approach to bi-level thresholding. Then we present its natural extension to multilevel thresholding. This multilevel threshold methodology segments the image into several distinct regions which correspond to a background and several objects.Segmentation experimental results and comparison with Otsu’s multilevel thresholding algorithm for the calculation of two and three thresholds are presented.     

Generating MDA’s platform independent model using URDAD

This paper formulates a set of minimal requirements for the Platform Independent Model (PIM) of the Model Driven Architecture (MDA). It then defines the Use Case, Responsibility Driven Analysis and Design methodology (URDAD) which provides a simple, algorithmic design methodology generating a PIM satisfying the specified PIM requirements.     

Student’s perceptions towards using e-learning via Facebook

Research on utilising social networks for teaching and learning is relatively scarce in the context of information systems. There is far more emphasis on studying the usage of social networks towards fulfilling individuals’ basic social needs. This study uses the unified theory of acceptance and use of technology (UTAUT2) to analyse students’ intention to use and use of e-learning via Facebook. It incorporates playfulness into the UTAUT2 model and categorises the determinants of intention to use e-learning via Facebook into three categories, namely, hedonic values, utilitarian values, and communication values. The data were collected in a two-stage survey from 170 undergraduate students, and the model was tested using structural equation modelling. We found that hedonic motivation, perceived playfulness, and performance expectancy were strong determinants of students’ intention to use e-learning, while habit and facilitating conditions all positively affected students’ use of e-learning via Facebook. The results of this study report new knowledge that academic institutions can utilise to create appropriate e-learning environments for teaching and learning. A number of theoretical and managerial implications for universities’ implementation technologies were also identified.     

Algorithms of increasing the calculation accuracy for an aircraft’s orientation angle

Algorithms for calculating an aircraft’s orientation angles based on the results of the numerical integration of Poisson’s and quaternion equations are proposed. The algorithms in the presence of random errors of the matrix elements of direction cosines and quaternions are characterized by a significantly higher accuracy in comparison to the formulas for solving the formulated problem. The results of testing the mathematical modeling data under random errors, which confirm the increased accuracy of the calculation of the orientation angles, are given.     

On the measurement of privacy as an attacker’s estimation error

A wide variety of privacy metrics have been proposed in the literature to evaluate the level of protection offered by privacy-enhancing technologies. Most of these metrics are specific to concrete systems and adversarial models and are difficult to generalize or translate to other contexts. Furthermore, a better understanding of the relationships between the different privacy metrics is needed to enable more grounded and systematic approach to measuring privacy, as well as to assist system designers in selecting the most appropriate metric for a given application. In this work, we propose a theoretical framework for privacy-preserving systems, endowed with a general definition of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. We show that our framework permits interpreting and comparing a number of well-known metrics under a common perspective. The arguments behind these interpretations are based on fundamental results related to the theories of information, probability, and Bayes decision.