Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Multi-modal Low-Resourced Speech keywords Recognition using Sequential Conv2D Nets

Advanced Neural Networks are widely used to recognize multi-modal conversational speech with significant improvements in accuracy automatically. Significantly, Convolutional Neural (CN) sheets have retreated cutting-edge performance in Automatic Voice Recognition (AVR) recently more appropriately in English; however, the Hindi language has not been explored and examined well on AVR systems. The work in this paper has exposed a 3-layered two-dimensional Sequential Convolutional neural architecture. The Sequential Conv2D is an end-to-end system that can instantaneously exploit speech signal spectral and temporal structures. The network has been trained and tested on different cepstral features such as Frequency and Time variant- Mel-Filters, Gamma-tone Filter Cepstral Quantities (GFCQ), Bark-Filter band Coefficients, and Spectrogram features of speech structures. The experiment was performed on two low-resourced speech command datasets; Hindi with 27,145 Speech Keywords developed by TIFR and 23,664 (1-second utterances) of English speech commands by Google TensorFlow and AIY English Speech Commands. The experimental outcome showed that the model achieves significant performance of Convolutional layers trained on spectrograms with 91.60% accuracy, compared to that achieved in other cepstral feature labels for English speech. However, the model achieved an accuracy of 69.65% for Hindi audio words in which BFCC features outperformed spectrogram features.
Creative Discovery using Quality-Diversity Search

In creative design, where aesthetics play a crucial role in determining the quality of outcomes, there are often multiple worthwhile possibilities, rather than a single "best" design. This challenge is compounded in the use of computational generative systems, where the sheer number of potential outcomes can be overwhelming. This paper introduces a method that combines evolutionary optimisation with AI-based image classification to perform quality-diversity search, allowing for the creative exploration of complex design spaces. The process begins by randomly sampling the genotype space, followed by mapping the generated phenotypes to a reduced representation of the solution space, as well as evaluating them based on their visual characteristics. This results in an elite group of diverse outcomes that span the solution space. The elite is then progressively updated via sampling and simple mutation. We tested our method on a generative system that produces abstract drawings. The results demonstrate that the system can effectively evolve populations of phenotypes with high aesthetic value and greater visual diversity compared to traditional optimisation-focused evolutionary approaches.
Evolutionary Optimization of a Focused Ultrasound Propagation Predictor Neural Network

The search for the optimal treatment plan of a focused ultrasound-based procedure is a complex multi-modal problem, trying to deliver the solution in clinically relevant time while not sacrificing the precision below a critical threshold. To test a solution, many computationally expensive simulations must be evaluated, often thousands of times. The recent renaissance of machine learning could provide an answer to this. Indeed, a state-of-the-art neural predictor of Acoustic Propagation through a human skull was published recently, speeding up the simulation significantly. The utilized architecture, however, could use some improvements in precision. To explore the design more deeply, we made an attempt to improve the solver by use of an evolutionary algorithm, challenging the importance of different building blocks. Utilizing Genetic Programming, we improved their solution significantly, resulting in a solver with approximately an order of magnitude better RMSE of the predictor, while still delivering solutions in a reasonable time frame. Furthermore, a second study was conducted to gauge the effects of the multi-resolution encoding on the precision of the network, providing interesting topics for further research on the effects of the memory blocks and convolution kernel sizes for PDE RCNN solvers.
An analysis of heuristic templates in Genetic Programming for one-dimensional cutting and packing problems

One-dimensional cutting stock and packing problems require determining a set of patterns that are applied a number of times each on raw material pieces to produce a number of customer orders. Among many other solving methods, greedy algorithms guided by heuristic rules stand out due to their low computational cost and ability to be adapted to sets of instances with similar structures. In this paper, we use genetic programming (GP) to evolve heuristics for the one-dimensional bin packing problem. We explore two greedy variants taken from the literature; in the first one, termed cut-by-cut, the heuristic rule is used to construct the pattern by selecting the most appropriate item that should be packed. In the second one, denoted as pattern-by-pattern, a number of patterns are randomly generated, and the heuristic selects the most appropriate one to be applied. We thoroughly analysed the problem's features to identify the relevant attributes of each greedy strategy. From these attributes, we exploited GP to evolve a number of heuristics adapted to a well-known benchmark set of one-dimensional bin packing instances. The experimental results provided interesting insights into the problem features and showed that the evolved heuristics are competitive with the state-of-the-art.
A New Acquisition Function for Multi-objective Bayesian Optimization: Correlated Probability of Improvement

Multi-objective Bayesian optimization is a sequential optimization strategy in which an optimizer searches for optimal solutions by maximizing an acquisition function. Most existing acquisition functions assume that objectives are independent, but none of them incorporates the correlations among objectives through an explicit formula for exact computation. This paper proposes a novel acquisition function, namely, correlated probability of improvement (cPoI), for bi-objective optimization problems. The cPoI method builds on the probability of improvement and addresses the correlations between objectives by utilizing 3 distinct approaches to compute the posterior covariance matrix from a multi-task Gaussian process. This paper presents both an explicit formula for exact computation of cPoI and a Monte Carlo method for approximating it. We evaluate the performance of the proposed cPoI against 4 state-of-the-art multi-objective optimization algorithms on 8 artificial benchmarks and 1 real-world problem. Our experimental results demonstrate the effectiveness of cPoI in achieving superior optimization performance.
Evolving Graphs with Cartesian Genetic Programming with Lexicase Selection

The automatic construction of an image filter is a difficult task for which many recent machine-learning methods have been proposed. Cartesian Genetic Programming (CGP) has been effectively used in image-processing tasks by evolving programs with a function set specialized for computer vision. Although standard CGP is able to construct understandable image filter programs, we hypothesize that explicitly using a mechanism to control the size of the generated filter programs would help reduce the size of the final solution while keeping comparable efficacy on a given task. It is indeed central to keep the graph size as contained as possible as it improves our ability to understand them and explain their inner functioning. In this work, we use the Lexicase selection as the mechanism to control the size of the programs during the evolutionary process, by allowing CGP to evolve solutions based on performance and on the size of such solutions. We extend Kartezio, a Cartesian Genetic Programming for computer vision tasks, to generate our programs. We found in our preliminary experiment that CGP with Lexicase selection is able to achieve similar performance to the standard CGP while keeping the size of the solutions smaller.
EA-based smartwatch application for training and assistance in cardiopulmonary resuscitation

Cardiopulmonary resuscitation (CPR) is critical to the prevention of death from cardiac arrest. Smartwatches can help first responders perform CPR correctly by collecting real-time acceleration data to determine compression rate and depth. We present an approach for a smartwatch application that uses motion data from the inertial sensors and an evolutionary algorithm ((μ + λ)-ES) to compute compression depth and frequency and provide corrective feedback. The application was optimized for out-of-hospital CPR and evaluated in a pilot study using a CPR training manikin as a reference system and an existing smartphone app for comparison.
An Adaptive Sequence-Based Selection Hyper-Heuristic for Application to Electric Bus Scheduling

Buses are important for public transportation and beneficial for the environment. However, diesel buses are significant polluters emitting greenhouse gases and particulates. Consequently, with the advent of electric vehicles there has been a drive to transition to electric buses. Key to this transition is to optimise electric bus fleets to reduce distance travelled whilst maintaining service levels. This is complex due to the added constraint of the limited range of electric buses. This paper considers the use of a Sequence-based Selection Hyper Heuristic (SSHH) method to solve this problem. Moreover, an adaptive SSHH (A-SSHH) technique is introduced which significantly improves upon SSHH. Indeed, bus fleet non-service distances and sizes are reduced by as much as 10% using A-SSHH over SSHH. Comparing with an optimised diesel bus fleet electric buses reduce carbon dioxide emissions by over 60% and importantly for fleet operators, energy costs are similarly reduced.
Implication of Size-Controlled Graphite Nanosheets as Building Blocks for Thermal Conductive Three-Dimensional Framework Architecture of Nanocarbons

ABSTRACT Preparation of three-dimensional (3D) networks has received significant attention as an effective approach for applications involving transport phenomena, such as thermal management materials, and several nanomaterials have been examined as potential building blocks of 3D networks for the improvement of heat conduction in polymer nanocomposites. For that purpose, nanocarbons such as graphene and graphite nanoplatelets have been spotlighted as suitable filler materials because of their excellent thermal conductivities (ca. 102–103 W·(m·K)−1 along their lateral axes) and morphological merits. However, the implications of morphological features such as the lateral length and thickness of graphene or graphene-like materials have not yet been identified. In this study, a controlled dissociation of bulk graphite to graphite nanosheets (GNSs) using a low-cost, ecofriendly bead mill process was extensively examined and, when configured in a 3D framework architecture formation, the size-controlled GNSs demonstrated that the thermal conductivities of a 3D interconnected framework of GNSs and the corresponding polymer nanocomposite were intimately correlated with the size of the GNSs, thus demonstrating the successful preparation of an efficient thermal management material without highly sophisticated efforts. The capability of controlling the lateral size and thickness of the GNSs as well as the use of a 3D interconnected framework architecture should greatly assist the commercialization of high-quality graphene-based thermal management materials in a scalable production process.
A Feature-Free Approach to Automated Algorithm Selection

This article summarises recent work in the domain of feature-free algorithm selection that was published in the Journal of Heuristics in January 2023, with the title 'Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches'. Specifically, we consider domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. We train two types of recurrent neural networks (RNN) to predict a packing heuristic in online bin-packing that use the sequence of item-sizes as input, i.e. no features are derived to describe the instance. This contrasts to typical approaches to algorithm-selection. The RNN approaches are shown to be capable of achieving within 5% of the oracle performance on between 80.88 and 97.63% of the instances, depending on the dataset. They are also shown to outperform classical machine learning models trained using derived features. In order to explain the observed result, we suggest that our methods perform well when the instances exhibit some implicit structure. To provide evidence for this, 14 new datasets with controllable levels of structure are generated, indicating that a critical threshold of structure is required before algorithm-selection delivers benefit.
Source enumeration via GBIC with a statistic for sphericity test in white Gaussian and non-Gaussian noise

We propose a source enumeration method via the generalised Bayesian information criterion (GBIC) based on a statistic for sphericity test in the white Gaussian and non-Gaussian noise under a large array with few samples. Instead of joint probability of observations or sample eigenvalue distribution, the proposed method is based on a statistic for testing the sphericity of a positive definite covariance matrix, to overcome the limitation of the Gaussian observations assumption. Under the white noise assumption, the covariance matrix of the noise subspace components of the observations is proportional to an identity matrix, and this identity structure can be tested by a statistic for sphericity test. The observations are decomposed into signal and noise subspace components under a presumptive number of sources. When the presumptive noise subspace components do not contain signals, the corresponding statistic for sphericity test will have a certain Gaussian distribution, and the number of sources can be estimated via the GBIC with the test statistic. Simulation results demonstrate that the proposed method provides high detection probability in both the Gaussian and the non-Gaussian noise, and performs better when the number of samples is less than the number of array sensors compared with other methods.
Configuring a Hierarchical Evolutionary Strategy Using Exploratory Landscape Analysis

Hierarchic Memetic Strategy (HMS) is a stochastic global optimizer designed to tackle highly multimodal problems. It consists of parallel running optimization methods organized in a tree hierarchy. Depending on the task, different algorithms can be utilized on each of the levels. In this paper, we incorporate into HMS's structure a mechanism for choosing its configuration based on information gathered by a set of Exploratory Landscape Analysis (ELA) methods and hyperparametric optimization. We compared the performance of such configured HMS with a portfolio of proven state-of-the-art algorithms on the suite of black-box optimization functions. The results of this work show the efficacy of HMS and provide a set of default parameters evaluated for algorithms users. The use of ELA methods to select the configuration of a composite algorithm extends their standard use as part of an algorithm selector and provides insight into the relationship between exploration and exploitation for different types of fitness functions.
Hybrid Rocket Engine Design Using Pairwise Ranking Surrogate-assisted Differential Evolution

Real-world optimization problems often require time-consuming fitness evaluations because of simulations or complex numerical calculations. A hybrid rocket engine (HRE) design problem is a time-consuming real-world application. HRE is a rocket engine that stores either the oxidizer or fuel in solid form and the other in liquid form. A surrogate model can reduce the number of expensive objective/constraint function calculations for such expensive optimization problems by searching for promising solutions. This study applies a new surrogate-assisted constraint-handling evolutionary algorithm, CHDE+ELDR, to solve the HRE design problem with fewer evaluations. CHDE+ELDR combines CHDE, a constraint-handling differential evolution, and ELDR, a pairwise ranking surrogate model. We conduct an experiment on the HRE design problem with limited evaluations and compare CHDE+ELDR with CHDE without a surrogate model. The experimental results show that CHDE+ELDR can consistently obtain high-quality HRE designs with fewer evaluations than simple CHDE.
A-Stable Block ETRs/ETR2s Methods for Stiff ODEs

We present a class of fourth and sixth order A-stable block extended trapezoidal rule of first kind (ETRs) and extended trapezoidal rule of second kind (ETR 2 s) methods which are found to be adequate for the numerical integration of stiff ordinary differential equations. The single continuous formulation of this methods are evaluated at some grid and interior points yielding the multi-discrete schemes which are implemented in block form thereby generating simultaneously approximate solutions at once without recourse to predictors. By this approach, the need for starters is eliminated. The stability properties of the block ETRs/ETR 2 s methods discussed and were shown to preserve the A-stability property of the trapezoidal rule, their absolute stability regions also presented. The newly derived block methods were implemented on five stiff systems of ordinary differential equations occurring in real life to show efficiency and accuracy .
Genetic Algorithm-based Feature Selection for Machine Learning System Diagnosing Sarcopenia with Survey Data

This study investigates whether a genetic algorithm (GA) can improve the performance of machine learning for sarcopenia diagnosis. An essential aspect of applying feature selection to machine learning for diagnosing sarcopenia is the selection of features that directly affect the diagnosis. To determine whether the GA can perform this logic effectively, we performed feature selection using the Korean Longitudinal Study of Aging (KLoSA) survey data. This study is significant because it implements feature selection using GA and shows that diagnosis performance is improved compared to other machine learning methods without feature selection. In addition, the results showed that GAs could improve the diagnosis of sarcopenia in future research.
Microlensing constraints on $10^{-10}M_\odot$-scale primordial black holes from high-cadence observation of M31 with Hyper Suprime-Cam

We use the Subaru Hyper Suprime-Cam (HSC) to conduct a high-cadence (2~min sampling) 7~hour long observation of the Andromeda galaxy (M31) to search for the microlensing magnification of stars in M31 due to intervening primordial black holes (PBHs) in the halo regions of the Milky Way (MW) and M31. The combination of an aperture of 8.2m, a field-of-view of 1.5 degree diameter, and excellent image quality ($\sim 0.6^{\prime\prime}$) yields an ideal dataset for the microlensing search. If PBHs in the mass range $M_{\rm PBH}=[10^{-13},10^{-6}]M_\odot$ make up a dominant contribution to dark matter (DM), the microlensing optical depth for a {\it single} star in M31 is $\tau\sim 10^{-4}$--$10^{-7}$, owing to the enormous volume and large mass content between M31 and the Earth. The HSC observation allows us to monitor more than tens of millions of stars in M31 and in this scenario we should find many microlensing events. To test this hypothesis, we extensively use an image subtraction method to efficiently identify candidate variable objects, and then monitor the light curve of each candidate with the high cadence data. Although we successfully identify a number of real variable stars such as eclipse/contact binaries and stellar flares, we find only one possible candidate of PBH microlensing whose genuine nature is yet to be confirmed. We then use this result to derive the most stringent upper bounds on the abundance of PBHs in the mass range. When combined with other observational constraints, our constraint rules out almost all the mass scales for the PBH DM scenario where all PBHs share a single mass scale.
Evolving Herding Behaviour Diversity in Robot Swarms

Behavioural diversity has been demonstrated as beneficial in biological social systems, such as insect colonies and human societies, as well as artificial systems such as large-scale software and swarm-robotics systems. Evolutionary swarm robotics is a popular experimental platform for demonstrating the emergence of various social phenomena and collective behaviour, including behavioural diversity and specialization. However, from an automated design perspective, the evolutionary conditions necessary to synthesize optimal collective behaviours (swarm-robotic controllers) that function across increasingly complex environments (difficult tasks), remains unclear. Thus, we introduce a comparative study of behavioural-diversity maintenance methods (swarm-controller extension of the MAP-Elites algorithm) versus those without behavioural diversity mechanisms (Steady-State Genetic Algorithm), as a means to evolve suitable degrees of behavioural diversity over increasingly difficult collective behaviour (sheep-dog herding) tasks. In support of previous work, experiment results demonstrate that behavioural diversity can be generated without specific speciation mechanisms or geographical isolation in the task environment, although the direct evolution of a functionally (behaviorally) diverse swarm does not yield high task performance.
Learning Behavior Trees by Evolution-Inspired Approaches

As a reactive and modular policy control architecture, Behavior Tree (BT) has been used in computer games and robotics for autonomous agents' task switching. However, constructing BTs manually for complex tasks requires expert domain-knowledge and is error-prone. As a solution, researchers have proposed to auto-construct BTs using evolutionary algorithms such as Genetic Programming (GP) and Grammatical Evolution (GE). Nevertheless, their effectiveness in practical situations is in doubt and there are different drawbacks in the application. In this paper, we present a novel BT evolutionary system that integrates both GE and GP as modules and auto-checks the complexity of a given task to select which module to use. In addition, our system collects BTs that are either previously generated or manually designed by the user, which are utilized to further improve the convergence speed and the quality of generated trees for new tasks.
Logatome and Sentence Recognition Related to Acoustic Parameters of Enclosures

The paper deals with relationship between speech recognition and objective parameters of enclosures. Six enclosures were chosen: a church, an assembly hall of a music school, two courtrooms of different volumes, a typical auditorium and a university concert hall. Dirac 4.1 software was used to record impulse responses (IRs) in the chosen measurement points of each enclosure. On this base, the following objective parameters of the enclosure were determined: Reverberation Time (RT), Early Decay Time (EDT), Weighted Clarity (C50) and Speech Transmission Index (STI). A convolution of the IRs with logatome tests and the Polish Sentence Test (PST) was made. Logatome recognition and speech reception threshold (SRT – i.e., SNR yielding 50% speech recognition) were evaluated and their dependence on the objective parameters were determined. Generally a linear relationship between logatome or SRT and RT or EDT was found. However, speech recognition was nonlinearly related (according to psychometric function) to STI values. The most sensitive range of the logatome and sentence recognition relative to STI changes corresponded to the middle range of STI values. Below and above this range, logatome and sentence recognition were much less dependent of STI changes .