Complex Problem Domains Research Articles

Pre-Processing of Categorical Features Within Medical Analysis Systems.

The complexity of the cancer problem domain presents challenges not only to the medical analysis systems tasked with its analysis, but also to the users of such systems. While it is desirable to assist users in operating these medical analysis systems, prior groundwork is required before this can be achieved, such as recognising patterns in the way users create certain analyses within these systems. In this paper, we use machine learning algorithms to analyse user behaviour patterns and attempt to predict the next user interaction within the CARESS medical analysis system. Since an appropriate pre-processing scheme is essential for the performance of these algorithms, we propose the usage of a Natural Language Processing (NLP)- inspired approach to preserve some semantic cohesion of the mostly categorical features of these user interactions. Furthermore, we propose to use a sliding window that contains information about the latest user interactions in combination with Latent Dirichlet Allocation (LDA) to extract a latent topic from these last interactions and use it as additional input to the machine learning models. We compare this pre-processing scheme with other approaches that utilise one-hot encoding and feature hashing. The results of our experiments show that the sliding window LDA scheme is a promising solution, that performs better for our use case than the other evaluated pre-processing schemes. Overall, our results provide an important piece for further research and development in the area of assisting users in operating analysis systems in complex problem domains.

On the Complementarity between CMMN and iStar in Complex Domain Modeling

Case Management Modeling and Notation (CMMN) and iStar are two distinct, multi-purposed modeling techniques that may be used to represent organizational challenges at separate levels of abstraction. CMMN, a flexible process-oriented technique, aims to extract knowledge that enhances the representational capacity of activity flows for a specific case. Conversely, the iStar framework adopts a goal-oriented modeling approach, effectively capturing the interplay of social actors and their influence on the attainment of organizational objectives. While prior studies have explored methods for integrating these techniques to attain a more comprehensive understanding of the organizational landscape, these remain primarily associated with a distinct level of the organizational matrix (i.e., CMMN for operations and tactics, and iStar for more strategic aspects). As such, any effort to evaluate their semantic proximity appears fragmented, as it deals only with the partial association of specific notations and elements. This article describes the conduct of a dual-purposed literature review to identify specific criteria that might accommodate a more holistic assessment of the two modeling techniques; these criteria are then employed as a guiding framework to construct a set of propositions that articulate the areas of complementarity and/or divergence between these two techniques, as identified in previous research. These propositions are subsequently subjected to validation by domain experts, leveraging a real-world case study in the educational domain. The results show that there can be areas of semantic convergence between the two techniques, suggesting their parallel use to effectively model complex domain problems. Overall, the present study aims to crystallize an approach for conducting complex modeling comparisons that transcends technical considerations.

Harmonic Mean Optimizer (HMO) for global problems solving

This study introduces the Harmonic Mean Optimizer (HMO), a novel meta-heuristic algorithm designed to address complex global optimization problems. The primary objective of this research is to develop an optimization technique that effectively balances exploration and exploitation without requiring extensive parameter tuning, thereby simplifying the optimization process and enhancing its robustness across various problem domains. The HMO employs a unique dual-fitness index that leverages the harmonic mean to assess both the quality and diversity of candidate solutions dynamically. This approach ensures a balanced search process that avoids premature convergence and improves the ability to find global optima. The methodology involves extensive benchmarking of the HMO against a comprehensive set of test functions, including 23 traditional functions from the Congress on Evolutionary Computation (CEC) 2017 test suite. The performance of the HMO is compared with that of established meta-heuristic algorithms, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to validate its efficacy in terms of convergence speed and solution accuracy. Additionally, the HMO is applied to several real-world engineering problems to demonstrate its practical utility. The results show that the HMO consistently outperforms the benchmark algorithms, achieving faster convergence and higher accuracy in finding optimal solutions across a diverse range of test problems. In practical applications, the HMO effectively optimized complex engineering tasks, achieving significant improvements in both solution quality and computational efficiency. These findings highlight the potential of the HMO as a versatile and powerful tool for global optimization tasks. the HMO offers a significant advancement in optimization methodologies by providing a robust, parameter-free approach that effectively balances exploration and exploitation. This study underscores the HMO’s applicability to a wide range of optimization challenges and sets the foundation for future research and development in enhancing and applying this innovative algorithm to more complex and diverse problem domains.

Cloud storage cost: a taxonomy and survey

Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.

Study of convergence speed of chaotic particle swarm optimization algorithm

AbstractThis study introduces the Chaotic Particle Swarm Optimization as an innovative variant of the traditional particle swarm optimization algorithm, addressing the issue of particle swarm optimization getting trapped in local minima with a low convergence characteristic during later iterations. Chaotic particle swarm optimization incorporates principles from chaos theory to enhance the swarm's exploration and exploitation capabilities. By introducing controlled chaotic behavior, particles exhibit more diverse and unpredictable movements in the search space, leading to improved global convergence and escape from local minima. The proposed method has been implemented and evaluated on benchmark problems to assess its effectiveness. The integration of chaos theory with particle swarm optimization offers promising opportunities for developing robust and efficient optimization techniques suitable for complex and dynamic problem domains in various real-world applications.

LEVA: Using Large Language Models to Enhance Visual Analytics.

Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.

A Convolutional Neural Network model for stress classification based on electrocardiogram data

Several studies have been published in recent years addressing the use of technologies for monitoring and diagnosing mental health. Stress disorders have aroused great interest among researchers and have been widely addressed in several studies due to the negative impacts they can cause to people's quality of life and the possibility of triggering serious illnesses that can seriously compromise physical health. Machine Learning techniques have proven to be a promising alternative to provide technology that can help to deal with challenges in the medical field and Deep Learning models particularly have provided excellent performance when dealing with complex problem domains, such as the classification of types and levels of stress. This study proposes a Convolutional Neural Network (CNN) model for classification of stress through the analysis of electrocardiogram data. The model was trained with a dataset generated from an experiment where individuals were monitored by electrocardiogram while performing stress- inducing tasks. The model achieved an accuracy rate of 97.5% in predictions and presented an adequate balance between performance and computational costs. The results obtained demonstrate the potential of Machine Learning techniques as tools to aid in monitoring and diagnosing mental health. Key Words: machine learning, deep learning, mental stress, electrocardiogram

ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories

We present ManuKnowVis, the result of a design study, in which we contextualize data from multiple knowledge repositories of a manufacturing process for battery modules used in electric vehicles. In data-driven analyses of manufacturing data, we observed a discrepancy between two stakeholder groups involved in serial manufacturing processes: Knowledge providers (e.g., engineers) have domain knowledge about the manufacturing process but have difficulties in implementing data-driven analyses. Knowledge consumers (e.g., data scientists) have no first-hand domain knowledge but are highly skilled in performing data-driven analyses. ManuKnowVis bridges the gap between providers and consumers and enables the creation and completion of manufacturing knowledge. We contribute a multi-stakeholder design study, where we developed ManuKnowVis in three main iterations with consumers and providers from an automotive company. The iterative development led us to a multiple linked view tool, in which, on the one hand, providers can describe and connect individual entities (e.g., stations or produced parts) of the manufacturing process based on their domain knowledge. On the other hand, consumers can leverage this enhanced data to better understand complex domain problems, thus, performing data analyses more efficiently. As such, our approach directly impacts the success of data-driven analyses from manufacturing data. To demonstrate the usefulness of our approach, we carried out a case study with seven domain experts, which demonstrates how providers can externalize their knowledge and consumers can implement data-driven analyses more efficiently.

Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks

Neural networks have revolutionised the way we approach problem solving across multiple domains; however, their effective design and efficient use of computational resources is still a challenging task. One of the most important factors influencing this process is model hyperparameters which vary significantly with models and datasets. Recently, there has been an increased focus on automatically tuning these hyperparameters to reduce complexity and to optimise resource utilisation. From traditional human-intuitive tuning methods to random search, grid search, Bayesian optimisation, and evolutionary algorithms, significant advancements have been made in this direction that promise improved performance while using fewer resources. In this article, we propose HyperGE, a two-stage model for automatically tuning hyperparameters driven by grammatical evolution (GE), a bioinspired population-based machine learning algorithm. GE provides an advantage in that it allows users to define their own grammar for generating solutions, making it ideal for defining search spaces across datasets and models. We test HyperGE to fine-tune VGG-19 and ResNet-50 pre-trained networks using three benchmark datasets. We demonstrate that the search space is significantly reduced by a factor of ~90% in Stage 2 with fewer number of trials. HyperGE could become an invaluable tool within the deep learning community, allowing practitioners greater freedom when exploring complex problem domains for hyperparameter fine-tuning.

Numerical geometric acoustics: An eikonal-based approach for modeling sound propagation in 3D environments

We present algorithms for solving high-frequency acoustic scattering problems in complex domains. The eikonal and transport partial differential equations from the WKB/geometric optic approximation of the Helmholtz equation are solved recursively to generate boundary conditions for a tree of eikonal/transport equation pairs, describing the phase and amplitude of a geometric optic wave propagating in a complicated domain, including reflection and diffraction. Edge diffraction is modeled using the uniform theory of diffraction. For simplicity, we limit our attention to domains with piecewise linear boundaries and a constant speed of sound. The domain is discretized into a conforming tetrahedron mesh. For the eikonal equation, we extend the jet marching method to tetrahedron meshes. Hermite interpolation enables second order accuracy for the eikonal and its gradient and first order accuracy for its Hessian, computed using cell averaging. To march the eikonal on an unstructured mesh, we introduce a new method of rejecting unphysical updates by considering Lagrange multipliers and local visibility. To handle accuracy degradation near caustics, we introduce several fast Lagrangian initialization algorithms. We store the dynamic programming plan uncovered by the marcher in order to propagate auxiliary quantities along characteristics. We introduce an approximate origin function which is computed using the dynamic programming plan, and whose 1/2-level set approximates the geometric optic shadow and reflection boundaries. We also use it to propagate geometric spreading factors and unit tangent vector fields needed to compute the amplitude and evaluate the high-frequency edge diffraction coefficient. We conduct numerical tests on a semi-infinite planar wedge to evaluate the accuracy of our method. We also show an example with a more realistic building model with challenging architectural features. Finally, we demonstrate a simple approach to extending the method to handle nonconstant speeds of sound by modifying the semi-Lagrangian updates to account for a varying speed.

Exploiting Predictability of Random Vector Functional Link Networks in Forecasting Quality of Service (QoS) Parameters of IoT-Based Web Services Data

Background: QoS parameters are volatile in nature and possess high nonlinearity, thus making the IoT-based service and recommendation process challenging. Methods: An efficient and accurate forecasting model is lacking in this area and needs to be explored. Though an artificial neural network is a prominent option for capturing such nonlinearities, its efficiency is limited by the structural complexity and iterative learning method. The random vector functional link network (RVFLN) significantly reduces the time complexity by randomly assigning input weights and biases without further modification. Only output layer weights are calculated iteratively by gradient methods or non-iteratively by least square methods. It is an efficient algorithm with low time complexity and can handle complex domain problems without compromising accuracy. Motivated by these characteristics, this article develops an RVFLN-based model for forecasting QoS parameter sequences. Results: Two real-world IoT-enabled web service dataset series are used in developing and evaluating the effectiveness of RVFLN-based forecasts in terms of three performance metrics. Conclusion: Experimental results, comparative studies, and statistical tests are conducted to establish the superiority of the proposed approach over four other similar forecasting techniques.

Water wave models using conformal coordinates

A review of nonlinear water wave problems in complex domains is presented. These include non-smooth highly variable bottom topographies and boundaries as for example forked channel regions. The physical and mathematical relevance of each case is discussed. The Schwarz–Christoffel mapping is the common modeling tool for these different problems. The mapping produces conformal coordinates which allow the problems’ dimension to be reduced and the weakly nonlinear partial differential system of equations to be simplified. Usually conformal mapping is adopted while preserving the Laplace operator. The main focus in the present review is to highlight some water wave problems where the underlying differential operators are not invariant under the conformal change of coordinates. The Schwarz–Christoffel mapping is used to produce a boundary fitted, curvilinear conformal coordinate system. The models reported include solitary waves on branching channels, and its reduction to nonlinear waves on graphs, as well as three dimensional surface-waves propagating over highly variable ridges. Of particular interest is nonlinear water waves on graphs, which has important applications and a scarce mathematical literature. Conformal mapping plays a crucial role regarding a systematic incorporation of forking angles into the equations, as not done before, and deducing a generalized compatibility condition at the graph nodes.

The smooth extension embedding method with Chebyshev polynomials

AbstractWe propose an implementation of the smooth extension embedding method (SEEM), first described by Agress and Guidotti's study, in the setting of Chebyshev polynomials. SEEM is a hybrid fictitious domain/collocation method which solves general boundary value problems in complex domains by recasting them as constrained optimization problems in a simple encompassing set. Previously, SEEM was introduced and implemented using a periodic box (read a torus) using Fourier series; here, it is implemented on a (non‐periodic) rectangle using Chebyshev polynomial expansions. This implementation has faster convergence on smaller grids. Numerical experiments will demonstrate that the method provides a simple, robust, efficient, and high order fictitious domain method which can solve elliptic and parabolic problems in complex geometries, with non‐constant coefficients, and for general boundary conditions. We consider applications to two and three dimensional boundary value problems as well as an initial boundary value problem via a genuinely space–time discretization.

A semi-implicit meshless method for incompressible flows in complex geometries

We present an exponentially convergent semi-implicit meshless algorithm for the solution of Navier-Stokes equations in complex domains. The algorithm discretizes partial derivatives at scattered points using radial basis functions (RBF) as interpolants. Higher-order polynomials are appended to the polyharmonic splines (PHS-RBF) and a collocation method is used to derive the interpolation coefficients. The interpolating kernels are then differentiated and the partial-differential equations are satisfied by collocation at the scattered points. The PHS-RBF interpolation is shown to be exponentially convergent with discretization errors decreasing as a high power of a representative distance between points. We present here a semi-implicit algorithm for time-dependent and steady state fluid flows in complex domains. At each time step, several iterations are performed to converge the momentum and continuity equations. A Poisson equation for pressure corrections is formulated by imposing divergence free condition on the iterated velocity field. At each time step, the momentum and pressure correction equations are repeatedly solved until the velocities and pressure converge to a pre-specified tolerance. We have demonstrated the convergence and discretization accuracy of the algorithm for two model problems and simulated four other complex problems. In all cases, the algorithm is stable for Courant numbers in excess of ten. The algorithm has the potential to accurately and efficiently solve many fluid flow and heat transfer problems in complex domains. An open source code Meshless Multi-Physics Software (MeMPhyS) [1] is available for interested users of the algorithm.

Hyper-Heuristics for Personnel Scheduling Domains

In real-life applications problems can frequently change or require small adaptations. Manually creating and tuning algorithms for different problem domains or different versions of a problem can be cumbersome and time-consuming. In this paper we consider several important problems with high practical relevance, which are Bus Driver Scheduling, Rotating Workforce Scheduling, and Minimum Shift Design. Instead of designing very specific solution methods, we propose to use the more general approach based on hyper-heuristics which take a set of simpler low-level heuristics and combine them to automatically create a fitting heuristic for the problem at hand. This paper presents a major study on applying hyper-heuristics to these domains, which contributes in three different ways: First, it defines new low-level heuristics for these scheduling domains, allowing to apply hyper-heuristics to them for the first time. Second, it provides a comparison of several state-of-the-art hyper-heuristics on those domains. Third, new best solutions for several instances of the different problem domains are found. These results show that hyper-heuristics are able to perform well even on very complex practical problem domains in the area of scheduling and, while being more general and requiring less problem-specific adaptation, can in several cases compete with specialized algorithms for the specific problems. These results help to improve industrial systems in use for solving different scheduling scenarios by allowing faster and easier adaptation to new problem variants.

Localized MFS for three‐dimensional acoustic inverse problems on complicated domains

AbstractThis paper proposes a semi‐analytical and local meshless collocation method, the localized method of fundamental solutions (LMFS), to address three‐dimensional (3D) acoustic inverse problems in complex domains. The proposed approach is a recently developed numerical scheme with the potential of being mathematically simple, numerically accurate, and requiring less computational time and storage. In LMFS, an overdetermined sparse linear system is constructed by using the known data at the nodes on the accessible boundary and by making the remaining nodes satisfy the governing equation. In the numerical procedure, the pseudoinverse of a matrix is solved via the truncated singular value decomposition, and thus the regularization techniques are not needed in solving the resulting linear system with a well‐conditioned matrix. Numerical experiments, involving complicated geometry and the high noise level, confirm the effectiveness and performance of the LMFS for solving 3D acoustic inverse problems.

A Semi-Implicit Meshless Method for Incompressible Flows in Complex Geometries

We present an exponentially convergent semi-implicit meshless algorithm for the solution of Navier-Stokes equations in complex domains. The algorithm discretizes partial derivatives at scattered points using radial basis functions as interpolants. Higher-order polynomials are appended to polyharmonic splines (PHS-RBF) and a collocation method is used to derive the interpolation coefficients. The interpolating kernels are then differentiated and the partial-differential equations are satisfied by collocation at the scattered points. The PHS-RBF interpolation is shown to be exponentially convergent with discretization errors decreasing as a high power of a representative distance between points. We present here a semi-implicit algorithm for time-dependent and steady state fluid flows in complex domains. At each time step, several iterations are performed to converge the momentum and continuity equations. A Poisson equation for pressure corrections is formulated by imposing divergence free condition on the iterated velocity field. At each time step, the momentum and pressure correction equations are repeatedly solved until the velocities and pressure converge to a pre-specified tolerance. We have demonstrated the convergence and discretization accuracy of the algorithm for two model problems and simulated three other complex problems. In all cases, the algorithm is stable for Courant numbers in excess of ten. The algorithm has the potential to accurately and efficiently solve many fluid flow and heat transfer problems in complex domains. An open source code Meshless Multi-Physics Software (MeMPhyS) is available for interested users of the algorithm.

A high-order accurate meshless method for solution of incompressible fluid flow problems

Meshless solution to differential equations using radial basis functions (RBF) is an alternative to grid based methods commonly used. Since the meshless method does not need an underlying connectivity in the form of control volumes or elements, issues such as grid skewness that adversely impact accuracy are eliminated. Gaussian, Multiquadrics and inverse Multiquadrics are some of the most popular RBFs used for the solutions of fluid flow and heat transfer problems. But they have additional shape parameters that have to be fine tuned for accuracy and stability. Moreover, they also face stagnation error when the point density is increased for accuracy. Recently, Polyharmonic splines (PHS) with appended polynomials have been shown to solve the above issues and give rapid convergence of discretization errors with the degree of appended polynomials. In this research, we extend the PHS-RBF method for the solution of incompressible Navier-Stokes equations. A fractional step method with explicit convection and explicit diffusion terms is combined with a pressure Poisson equation to satisfy the momentum and continuity equations. Systematic convergence tests have been performed for five model problems with two of them having analytical solutions. We demonstrate fast convergence both with refinement of number of points and degree of appended polynomials. The method is further applied to solve problems such as lid-driven cavity and vortex shedding over circular cylinder. We have also analyzed the performance of this approach for solution of Euler equations. The proposed method shows promise to solve fluid flow and heat transfer problems in complex domains with high accuracy.

Two-step computational aeroacoustics approach for underhood cooling fan application

Aeroacoustic noise is one of the important characteristics of the fan design. Computational Aeroacoustics (CAA) can provide better design options without relying on physical prototypes and reduce the development time and cost. There are two ways of performing CAA analysis; one-step and two-step approach. In one-step CAA, air flow and acoustic analysis are carried out in a single software. In two-step approach, air flow and acoustic analysis are carried out in separate software. Two-step CAA approach can expedite the calculation process and can be implemented in larger and complex domain problems. For the work presented in this paper, a mockup of an underhood cooling fan was designed. The sound pressure levels were measured for different installation configurations. The sound pressure level for one of the configurations was calculated with two-step approach and compared with test data. The compressible fluid flow field was first computed in a commercially available computational fluid dynamics software. This flow field was imported in a separate software where fan noise sources were computed and further used to predict the sound pressure levels at various microphone locations. The results show an excellent correlation between test and simulation for both tonal and broadband components of the fan noise.

SUPPORTING HUMAN-CENTERED DESIGN IN PSYCHOLOGICALLY DISTANT PROBLEM DOMAINS: THE DESIGN FOR CYBERSECURITY CARDS

AbstractIncreasingly digital products and services make cybersecurity a crucial issue for designers. However, human-centered designers struggle to consider it in their work, partially a consequence of the high psychological distance between designers and cybersecurity. In this work, we build on the Design for Cybersecurity (DfC) Cards, an intervention to help designers consider cybersecurity, and examine a project-based design course to understand how and why specific DfC cards were used. Three findings result. First, designers found the intervention useful across all design phases and activities. Second, the cards helped design teams refocus their attention on the problem domain and project outcome. Third, we identify a need for support in framing and converging during user research, opportunity identification, and prototyping. We argue that the psychological distance between designers and the problem space of cybersecurity partially explains these findings, and ultimately exacerbates existing challenges in the design process. These findings suggest that design interventions must consider the psychological distance between designer and problem space, and have application in design practice across many complex problem domains.