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Metaheuristic algorithms are essential for solving complex optimization problems in different fields. However, the difficulty in comparing and rating these algorithms remains due to the wide range of performance metrics and problem dimensions usually involved. On the other hand, nonparametric statistical methods and post hoc tests are time-consuming, especially when we only need to identify the top performers among many algorithms. The Hierarchical Rank Aggregation (HRA) algorithm aims to efficiently rank metaheuristic algorithms based on their performance across many criteria and dimensions. The HRA employs a hierarchical framework that begins with collecting performance metrics on various benchmark functions and dimensions. Rank-based normalization is employed for each performance measure to ensure comparability and the robust TOPSIS aggregation is applied to combine these rankings at several hierarchical levels, resulting in a comprehensive ranking of the algorithms. Our study uses data from the CEC 2017 competition to demonstrate the robustness and efficacy of the HRA framework. It examines 30 benchmark functions and evaluates the performance of 13 metaheuristic algorithms across five performance indicators in four distinct dimensions. This presentation highlights the potential of the HRA to enhance the interpretation of the comparative advantages and disadvantages of various algorithms by simplifying practitioners' choices of the most appropriate algorithm for certain optimization problems., Comment: 13 pages, 1 figure

In today's era, Neural Networks (NN) are applied in various scientific fields such as robotics, medicine, engineering, etc. However, the predictions of neural networks themselves contain a degree of uncertainty that must always be taken into account before any decision is made. This is why many researchers have focused on developing different ways to quantify the uncertainty of neural network predictions. Some of these methods are based on generating prediction intervals (PI) via neural networks for the requested target values. The SEF (Shifting the Error Function) method presented in this paper is a new method that belongs to this category of methods. The proposed approach involves training a single neural network three times, thus generating an estimate along with the corresponding upper and lower bounds for a given problem. A pivotal aspect of the method is the calculation of a parameter from the initial network's estimates, which is then integrated into the loss functions of the other two networks. This innovative process effectively produces PIs, resulting in a robust and efficient technique for uncertainty quantification. To evaluate the effectiveness of our method, a comparison in terms of successful PI generation between the SEF, PI3NN and PIVEN methods was made using two synthetic datasets., Comment: The paper has been accepted at the 2024 International Conference on Computer and Applications (ICCA24), Cairo, Egypt, December 17-19, 2024. https://icca-conf.info/icca-2024

In this work, we continue the investigation of certain enrichments of dual algebraic structures in monoidal double categories, that was initiated in [Vas19]. First, we re-visit monads and comonads in double categories and establish a tensored and cotensored enrichment of the former in the latter, under general conditions. These include monoidal closedness and local presentability of the double category, notions that are proposed as tools required for our main results, but are of interest in their own right. The natural next step involves categories of the newly introduced modules for monads and comodules for comonads in double categories. After we study their main categorical properties, we establish a tensored and cotensored enrichment of modules in comodules, as well as an enriched fibration structure that involves (co)modules over (co)monads in double categories. Applying this abstract double categorical framework to the setting of V-matrices produces an enrichment of the category of V-enriched modules (fibred over V-categories) in V-enriched comodules (opfibred over V-cocategories), which is the many-object generalization of the respective result for modules (over algebras) and comodules (over coalgebras) in monoidal categories., Comment: 58 pages; minor fixes throughout the manuscript

Flood-induced deformation of the bed topography of fluvial meandering rivers could lead to river bank displacement, structural failure of the infrastructures, and the propagation of scour or deposition features. The assessment of sediment transport in large-scale meanders is, therefore, a key environmental issue. High-fidelity numerical models provide powerful tools for such assessments. However, high-fidelity simulations of large-scale rivers using the coupled flow and morphodynamics modules can be computationally expensive, owing to the costly two-way coupling between turbulence and bed morphodynamics. This study seeks to present a novel machine learning approach, which is trained using coupled large-eddy simulation (LES) and morphodynamics results. The proposed machine learning approach predicts bed shear stress and equilibrium bed morphology of large-scale meanders under bankfull flow conditions at a fraction of the cost of coupled LES-morphodynamics. We developed and evaluated the performance of a convolutional neural network autoencoder (CNNAE) algorithm to generate high-fidelity bed shear stress and equilibrium morphology of large-scale meandering rivers. The CNNAE algorithm utilizes instantaneous shear stress distribution and change of bed elevation of high-fidelity simulation results, along with geometric parameters of meanders as inputs to predict mean bed shear stress distribution and equilibrium bed elevation of rivers. The results demonstrated the feasibility, accuracy, and efficiency of the proposed CNNAE algorithm.

This study introduces a novel approach for learning mixtures of Markov chains, a critical process applicable to various fields, including healthcare and the analysis of web users. Existing research has identified a clear divide in methodologies for learning mixtures of discrete and continuous-time Markov chains, while the latter presents additional complexities for recovery accuracy and efficiency. We introduce a unifying strategy for learning mixtures of discrete and continuous-time Markov chains, focusing on hitting times, which are well defined for both types. Specifically, we design a reconstruction algorithm that outputs a mixture which accurately reflects the estimated hitting times and demonstrates resilience to noise. We introduce an efficient gradient-descent approach, specifically tailored to manage the computational complexity and non-symmetric characteristics inherent in the calculation of hitting time derivatives. Our approach is also of significant interest when applied to a single Markov chain, thus extending the methodologies previously established by Hoskins et al. and Wittmann et al. We complement our theoretical work with experiments conducted on synthetic and real-world datasets, providing a comprehensive evaluation of our methodology.

This study proposes a novel machine learning (ML) methodology for the efficient and cost-effective prediction of high-fidelity three-dimensional velocity fields in the wake of utility-scale turbines. The model consists of an auto-encoder convolutional neural network with U-Net skipped connections, fine-tuned using high-fidelity data from large-eddy simulations (LES). The trained model takes the low-fidelity velocity field cost-effectively generated from the analytical engineering wake model as input and produces the high-fidelity velocity fields. The accuracy of the proposed ML model is demonstrated in a utility-scale wind farm for which datasets of wake flow fields were previously generated using LES under various wind speeds, wind directions, and yaw angles. Comparing the ML model results with those of LES, the ML model was shown to reduce the error in the prediction from 20% obtained from the GCH model to less than 5%. In addition, the ML model captured the non-symmetric wake deflection observed for opposing yaw angles for wake steering cases, demonstrating a greater accuracy than the GCH model. The computational cost of the ML model is on par with that of the analytical wake model while generating numerical outcomes nearly as accurate as those of the high-fidelity LES.

In this work, we introduce a novel evaluation framework for generative models of graphs, emphasizing the importance of model-generated graph overlap (Chanpuriya et al., 2021) to ensure both accuracy and edge-diversity. We delineate a hierarchy of graph generative models categorized into three levels of complexity: edge independent, node independent, and fully dependent models. This hierarchy encapsulates a wide range of prevalent methods. We derive theoretical bounds on the number of triangles and other short-length cycles producible by each level of the hierarchy, contingent on the model overlap. We provide instances demonstrating the asymptotic optimality of our bounds. Furthermore, we introduce new generative models for each of the three hierarchical levels, leveraging dense subgraph discovery (Gionis & Tsourakakis, 2015). Our evaluation, conducted on real-world datasets, focuses on assessing the output quality and overlap of our proposed models in comparison to other popular models. Our results indicate that our simple, interpretable models provide competitive baselines to popular generative models. Through this investigation, we aim to propel the advancement of graph generative models by offering a structured framework and robust evaluation metrics, thereby facilitating the development of models capable of generating accurate and edge-diverse graphs.

Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances? Many real-world problems can be cast as graph inference tasks where the graph representation could capture complex relational phenomena (e.g., transactions among financial accounts in a journal entry), along with metadata reflecting tabular features (e.g. approver, effective date, etc.). While numerous anomaly detectors based on Graph Neural Networks (GNNs) have been proposed, none are capable of directly handling directed graphs with multi-edges and self-loops. Furthermore, the simultaneous handling of relational and tabular features remains an unexplored area. In this work we propose ADAMM, a novel graph neural network model that handles directed multi-graphs, providing a unified end-to-end architecture that fuses metadata and graph-level representation learning through an unsupervised anomaly detection objective. Experiments on datasets from two different domains, namely, general-ledger journal entries from different firms (accounting) as well as human GPS trajectories from thousands of individuals (urban mobility) validate ADAMM's generality and detection effectiveness of expert-guided and ground-truth anomalies. Notably, ADAMM outperforms existing baselines that handle the two data modalities (graph and metadata) separately with post hoc synthesis efforts., Comment: Accepted at IEEE BigData 2023

We introduce a novel approach for testing static typing implementations based on the concept of API-driven program synthesis. The idea is to synthesize type-intensive but small and well-typed programs by leveraging and combining application programming interfaces (APIs) derived from existing software libraries. Our primary insight is backed up by real-world evidence: a significant number of compiler typing bugs are caused by small test cases that employ APIs from the standard library of the language under test. This is attributed to the inherent complexity of the majority of these APIs, which often exercise a wide range of sophisticated type-related features. The main contribution of our approach is the ability to produce small client programs with increased feature coverage, without bearing the burden of generating the corresponding well-formed API definitions from scratch. To validate diverse aspects of static typing procedures (i.e., soundness, precision of type inference), we also enrich our API-driven approach with fault-injection and semantics-preserving modes, along with their corresponding test oracles. We evaluate our implemented tool, Thalia on testing the static typing implementations of the compilers for three popular languages, namely, Scala, Kotlin, and Groovy. Thalia has uncovered 84 typing bugs (77 confirmed and 22 fixed), most of which are triggered by test cases featuring APIs that rely on parametric polymorphism, overloading, and higher-order functions. Our comparison with state-of-the-art shows that Thalia yields test programs with distinct characteristics, offering additional and complementary benefits.

The healthcare sector is increasingly vulnerable to cyberattacks due to its growing digitalization. Patient data, including medical records and financial information, are at risk, potentially leading to identity theft and patient safety concerns. The European Union and other organizations identify key areas for healthcare system improvement, yet the industry still grapples with inadequate security practices. In response, the HEIR project offers a comprehensive cybersecurity approach, promoting security features from various regulatory frameworks and introducing tools such as the Secure Healthcare Framework and Risk Assessment for Medical Applications (RAMA). These measures aim to enhance digital health security and protect sensitive patient data while facilitating secure data access and privacy-aware techniques. In a rapidly evolving threat landscape, HEIR presents a promising framework for healthcare cybersecurity., Comment: 70 pages, 48 figures

Dense subgraph discovery methods are routinely used in a variety of applications including the identification of a team of skilled individuals for collaboration from a social network. However, when the network's node set is associated with a sensitive attribute such as race, gender, religion, or political opinion, the lack of diversity can lead to lawsuits. In this work, we focus on the problem of finding a densest diverse subgraph in a graph whose nodes have different attribute values/types that we refer to as colors. We propose two novel formulations motivated by different realistic scenarios. Our first formulation, called the densest diverse subgraph problem (DDSP), guarantees that no color represents more than some fraction of the nodes in the output subgraph, which generalizes the state-of-the-art due to Anagnostopoulos et al. (CIKM 2020). By varying the fraction we can range the diversity constraint and interpolate from a diverse dense subgraph where all colors have to be equally represented to an unconstrained dense subgraph. We design a scalable $\Omega(1/\sqrt{n})$-approximation algorithm, where $n$ is the number of nodes. Our second formulation is motivated by the setting where any specified color should not be overlooked. We propose the densest at-least-$\vec{k}$-subgraph problem (Dal$\vec{k}$S), a novel generalization of the classic Dal$k$S, where instead of a single value $k$, we have a vector ${\mathbf k}$ of cardinality demands with one coordinate per color class. We design a $1/3$-approximation algorithm using linear programming together with an acceleration technique. Computational experiments using synthetic and real-world datasets demonstrate that our proposed algorithms are effective in extracting dense diverse clusters., Comment: Accepted to KDD 2023

In meandering rivers, interactions between flow, sediment transport, and bed topography affect diverse processes, including bedform development and channel migration. Predicting how these interactions affect the spatial patterns and magnitudes of bed deformation in meandering rivers is essential for various river engineering and geoscience problems. Computational fluid dynamics simulations can predict river morphodynamics at fine temporal and spatial scales but have traditionally been challenged by the large scale of natural rivers. We conducted coupled large-eddy simulation (LES) and bed morphodynamics simulations to create a unique database of hydro-morphodynamic datasets for 42 meandering rivers with a variety of planform shapes and large-scale geometrical features that mimic natural meanders. For each simulated river, the database includes (i) bed morphology, (ii) three-dimensional mean velocity field, and (iii) bed shear stress distribution under bankfull flow conditions. The calculated morphodynamics results at dynamic equilibrium revealed the formation of scour and deposition patterns near the outer and inner banks, respectively, while the location of point bars and scour regions around the apexes of the meander bends is found to vary as a function of the radius of curvature of the bends to the width ratio. A new mechanism is proposed that explains this seemingly paradoxical finding. The high-fidelity simulation results generated in this work provide researchers and scientists with a rich numerical database for morphodynamics and bed shear stress distributions in large-scale meandering rivers to enable systematic investigation of the underlying phenomena and support a range of river engineering applications.