<abstract xml:lang="en"><title style='display:none'>Control flow graphs and code coverage</title>The control flow of programs can be represented by directed graphs. In this paper we provide a uniform and detailed formal basis for control flow graphs combining known definitions and results with new aspects. Two graph reductions are defined using only syntactical information about the graphs, but no semantical information about the represented programs. We prove some properties of reduced graphs and also about the paths in reduced graphs. Based on graphs, we define statement coverage and branch coverage such that coverage notions correspond to node coverage, and edge coverage, respectively.</abstract>

Control flow graphs and code coverage

<abstract><title style='display:none'>Abstract</title>In this work a new algorithm for quaternion-based spatial rotation is presented which reduces the number of underlying real multiplications. The performing of a quaternion-based rotation using a rotation matrix takes 15 ordinary multiplications, 6 trivial multiplications by 2 (left-shifts), 21 additions, and 4 squarings of real numbers, while the proposed algorithm can compute the same result in only 14 real multiplications (or multipliers—in a hardware implementation case), 43 additions, 4 right-shifts (multiplications by 1/4), and 3 left-shifts (multiplications by 2).</abstract>

An Algorithm for Quaternion–Based 3D Rotation

<abstract><title style='display:none'>Abstract</title> Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.</abstract>

Centre for Informatics and Applied Optimization School of Science, Information Technology and Engineering, University of Ballarat, Victoria 3353, Australia

Victoria Research Laboratory National ICT Australia, Victoria 3010, Australia

Learning the naive Bayes classifier with optimization models

<abstract xml:lang="en"><title style='display:none'>Circular Object Detection Using a Modified Hough Transform</title>A practical modification of the Hough transform is proposed that improves the detection of low-contrast circular objects. The original circular Hough transform and its numerous modifications are discussed and compared in order to improve both the efficiency and computational complexity of the algorithm. Medical images are selected to verify the algorithm. In particular, the algorithm is applied to localize cell nuclei of cytological smears visualized using a phase contrast microscope.</abstract>

Circular Object Detection Using a Modified Hough Transform

<abstract xml:lang="en"><title style='display:none'>K3M: A universal algorithm for image skeletonization and a review of thinning techniques</title>This paper aims at three aspects closely related to each other: first, it presents the state of the art in the area of thinning methodologies, by giving descriptions of general ideas of the most significant algorithms with a comparison between them. Secondly, it proposes a new thinning algorithm that presents interesting properties in terms of processing quality and algorithm clarity, enriched with examples. Thirdly, the work considers parallelization issues for intrinsically sequential algorithms of thinning. The main advantage of the suggested algorithm is its universality, which makes it useful and versatile for a variety of applications.</abstract>

Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Cracow, Poland

Faculty of Computer Science, Bialystok Technical University, ul. Wiejska 45 A, 15-351 Białystok, Poland

Faculty of Mathematics and Informatics, University of Białystok, ul. M. Skłodowskiej-Curie 14, 15-097 Białystok, Poland

K3M: A universal algorithm for image skeletonization and a review of thinning techniques

<abstract>In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the <italic>k</italic>-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.</abstract>

Department of Systems Design and Informatics Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, Japan

Department of Systems Design and Informatics Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, Japan, School of Electronic Engineering Dublin City University, Glasnevin, Dublin, Ireland

A rainfall forecasting method using machine learning models and its application to the Fukuoka city case

<abstract>
<title style='display:none'>Abstract</title>
Learning to perform abstract reasoning often requires decomposing the task in question into intermediate subgoals that are not specified upfront, but need to be autonomously devised by the learner. In Raven progressive matrices (RPMs), the task is to choose one of the available answers given a context, where both the context and answers are composite images featuring multiple objects in various spatial arrangements. As this high-level goal is the only guidance available, learning to solve RPMs is challenging. In this study, we propose a deep learning architecture based on the transformer blueprint which, rather than directly making the above choice, addresses the subgoal of predicting the visual properties of individual objects and their arrangements. The multidimensional predictions obtained in this way are then directly juxtaposed to choose the answer. We consider a few ways in which the model parses the visual input into tokens and several regimes of masking parts of the input in self-supervised training. In experimental assessment, the models not only outperform state-of-the-art methods but also provide interesting insights and partial explanations about the inference. The design of the method also makes it immune to biases that are known to be present in some RPM benchmarks.
</abstract>

jakub.k.kwiatkowski@doctorate.put.poznan.pl

Learning Abstract Visual Reasoning Via Task Decomposition: A Case Study in Raven Progressive Matrices

<abstract>
<title style='display:none'>Abstract</title>
We consider an extension of Lagrangian relaxation methods for solving the total weighted tardiness scheduling problem on a single machine. First, we investigate a straightforward relaxation method and decompose it into upper and lower subproblems. For the upper subproblem we propose an alternative solving method in the form of a local search metaheuristic. We also introduce a scaling technique by arbitrary numbers to reduce the complexity of the problem and confront it with greatest common divisor scaling. Next, we propose a novel alternative relaxation approach based on aggregating constraints. We discuss the properties and implementation of this new approach and a technique to further reduce its computational complexity. We perform a number of computer experiments on instances based on the OR-Library generation scheme to illustrate and ascertain the numerical properties of the proposed methods. The results indicate that for larger instances the proposed alternative relaxation and scaling approaches have a much better convergence rate with little to no decrease in solution quality. The results also show that the proposed local-search metaheuristic is a viable alternative to the existing solving methods.
</abstract>

New Ideas in Lagrangian Relaxation for a Scheduling Problem with the weighted Tardiness Criterion

<abstract>
<title style='display:none'>Abstract</title>
Epistemic bootstrap is a resampling algorithm that generates bootstrap real-valued samples based on some epistemic fuzzy data input. We apply this method as a universal basis for various statistical tests which can be then directly used for fuzzy random variables. Two classical goodness-of-fit tests are considered as an example to examine the suggested methodology for both synthetic and real data. The proposed approach is also compared with two other goodness-of-fit tests dedicated directly to fuzzy data.
</abstract>

Faculty of Mathematics and Information Science, Warsaw University of Technology

Bootstrapped Tests for Epistemic Fuzzy Data

<abstract>
<title style='display:none'>Abstract</title>
We introduce an algebraically active disturbance rejection-based control solution for the trajectory tracking problem of an uncertain second-order flat system with unknown external disturbances. To this end, we first algebraically identify the system’s unknown dynamics and the external disturbances with a linear set of time-varying integral expressions for the output and the control signal. We use the identified dynamics on an online feedback cancellation scheme to linearize the second-order system and cancel the uncertainties. With a proportional-integral controller we stabilize the linearized system without the need to estimate the velocity and have feedback from it. We carry out the stability analysis using linear systems theory. Finally, we evaluate the effectiveness of the proposed controller in a partially known 2-DOF manipulator.
</abstract>

Faculty of Physical and Mathematical Sciences, Autonomous University of Nuevo Leon

Algebraic Active Disturbance Rejection to Control a Generalized Uncertain Second–Order Flat System

<abstract>
<title style='display:none'>Abstract</title>
This paper addresses the challenge of managing state constraints in vehicle platoons, including maintaining safe distances and aligning velocities, which are key factors that contribute to performance degradation in platoon control. Traditional platoon control strategies, which rely on a constant time-headway policy, often lead to deteriorated performance and even instability, primarily during dynamic traffic conditions involving vehicle acceleration and deceleration. The underlying issue is the inadequacy of these methods to adapt to variable time-delays and to accurately modulate the spacing and speed among vehicles. To address these challenges, we propose a dynamic adjustment neural network (DANN) based cooperative control scheme. The proposed strategy employs neural networks to continuously learn and adjust to time varying conditions, thus enabling precise control of each vehicle’s state within the platoon. By integrating a DANN into the platoon control system, we ensure that both velocity and inter-vehicular spacing adapt in response to real-time traffic dynamics. The efficacy of our proposed control approach is validated using both Lyapunov stability theory and numeric simulation, which confirms substantial gains in stability and velocity tracking of the vehicle platoon.
</abstract>

Dynamic Adjustment Neural Network–Based Cooperative Control for Vehicle Platoons with State Constraints

<abstract>
<title style='display:none'>Abstract</title>
In this study, we investigate several online and semi-online scheduling problems related to two hierarchical machines with a common due date to maximize the total early work. For the pure online case, we design an optimal online algorithm with a competitive ratio of 
<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_amcs-2024-0018_ieq_001.png"/>
. Additionally, for the cases when the largest processing time is known, we give optimal algorithms with a competitive ratio of 6/5 if the largest job is a lower-hierarchy one, and of <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_amcs-2024-0018_ieq_002.png"/> − 1 if the largest job is a higher-hierarchy one.
</abstract>

Online and Semi–Online Scheduling on Two Hierarchical Machines with a Common Due Date to Maximize the Total Early Work

<abstract>In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1<italic>×N </italic>and <italic>N ×N </italic>comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms.</abstract>

Institute of Informatics Wrocław University of Technology, Wybrzeźe Wyspiańskiego 27, 50-370 Wrocław, Poland

Department of Spatial Management
Wrocław University of Environmental and Life Sciences, ul. Norwida 25/27, 50-375 Wrocław, Poland

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

<abstract>This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace.</abstract>

An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

<abstract><title style='display:none'>Abstract</title> In recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.</abstract>

Centre for Mobile Cloud Computing Research, Faculty of Computer Science and Information Technology University of Malaya, Kuala Lumpur 50603, Malaysia

School of Information Technology and Engineering VIT University, Vellore 632014, India

A comprehensive survey on formal concept analysis, its research trends and applications

<abstract><title style='display:none'>Abstract</title> A solution for fault tolerant control (FTC) of a quadrotor unmanned aerial vehicle (UAV) is proposed. It relies on model reference-based control, where a reference model generates the desired trajectory. Depending on the type of reference model used for generating the reference trajectory, and on the assumptions about the availability and uncertainty of fault estimation, different error models are obtained. These error models are suitable for passive FTC, active FTC and hybrid FTC, the latter being able to merge the benefits of active and passive FTC while reducing their respective drawbacks. The controller is generated using results from the robust linear parameter varying (LPV) polytopic framework, where the vector of varying parameters is used to schedule between uncertain linear time invariant (LTI) systems. The design procedure relies on solving a set of linear matrix inequalities (LMIs) in order to achieve regional pole placement and H∞ norm bounding constraints. Simulation results are used to compare the different FTC strategies.</abstract>

Automatic Control Department Polytechnic University of Catalonia (UPC), Rambla de Sant Nebridi, 11, 08222 Terrassa, Spain

Institute of Robotics and Industrial Informatics UPC-CSIC, Carrer de Llorens i Artigas, 4-6, 08028 Barcelona, Spain

Robust Quasi–LPV Model Reference FTC of a Quadrotor Uav Subject to Actuator Faults

<abstract><title style='display:none'>Abstract</title>The paper deals with the modeling and fault-tolerant control of a real battery assembly system which is under implementation at the RAFI GmbH company (one of the leading electronic manufacturing service providers in Germany). To model and control the battery assembly system, a unified max-plus algebra and model predictive control framework is introduced. Subsequently, the control strategy is enhanced with fault-tolerance features that increase the overall performance of the production system being considered. In particular, it enables tolerating (up to some degree) mobile robot, processing and transportation faults. The paper discusses also robustness issues, which are inevitable in real production systems. As a result, a novel robust predictive fault-tolerant strategy is developed that is applied to the battery assembly system. The last part of the paper shows illustrative examples, which clearly exhibit the performance of the proposed approach.</abstract>

Department of Research & Development, RAFI GmbH Co. KG, Ravensburger Straße, 128–134, D-88276 Berg/Ravensburg, Germany

Institute of Control and Computation Engineering, University of Zielona Góra, ul. prof. Z. Szafrana 2, 65-516 Zielona Góra, Poland

Faculty of Mechanical Engineering, University of Applied Sciences Ravensburg–Weingarten, Building D., Doggenriedstraße, Weingarten, Germany

Towards Robust Predictive Fault–Tolerant Control for a Battery Assembly System

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International Journal of Applied Mathematics and Computer Science

Journal of the University of Zielona Góra

 The International Journal of Applied Mathematics and Computer Science is a quarterly published in Poland since 1991 by the University of Zielona Góra in partnership with...

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