Reviewerly/exHarmony huggingface dataset:
Data-stitching algorithm based on elasticity

Data-stitching algorithms are widely used to combine multiple sequential data or extend the working area of measurement systems. However, stitched data by the least squares method have inconsistency at the intersections due to their measurement errors, mainly drift error. We propose a new stitching algorithm, to the best of our knowledge, that considers sequential data as an elastic body. We examine the algorithm with several types of sequential data and confirm mitigating of error of drift.
Training Variational Quantum Circuits through Genetic Algorithms

Recently, Variational Quantum Circuits (VQCs) are attracting considerable attention among quantum algorithms thanks to their robustness to the noise characterizing the current quantum devices. In detail, VQCs involve parameterized quan-tum circuits to be trained by means of a classical optimizer that makes queries to the quantum device. VQCs play a key role in several applications including quantum classifiers where the Hilbert space is used as feature space. Currently, the most used classical optimizer to learn V QCs is the gradient descent method. However, the so-called barren plateaus issue causes gradients of cost functions to become exceedingly small as the dimension of the classification problem is increased. As consequence, gradient descent method could be not efficient in real-world classification problems. This paper proposes to apply Genetic Algorithms (GAs) to train VQCs used as quantum classifiers. As shown in the experiments, the application of GAs results in accurate solutions obtained with a reduced number of queries to quantum devices.
<i>Mordo</i>: Silent Command Recognition Through Lightweight Around-Ear Biosensors

The prevalence of smart devices encourages increasing requirements of wearable human–computer interactions. To improve user acceptance, such interactions require easy-to-manipulate and unobtrusive characteristics. In this article, we, for the first time, propose to recognize silent commands through a lightweight and around-ear biosensing system Mordo that can be easily integrated with earphones, manipulate smart devices, and minimize social awkwardness. In particular, we first determine the empirical principles of constructing commands and experimentally screen the commands based on the around-ear configuration. Second, we select the optimal around-ear sensor configuration according to the single-channel signal-to-noise ratios (SNRs) and classification accuracies. Third, we propose a multistream CNN-LSTM network to learn the spatiotemporal mapping between the around-ear signals and commands. Finally, extensive experiments have been conducted to evaluate the feasibility and stability. The results indicate an averaged accuracy of 89.66% that outperforms other algorithms of similar tasks. The stability tests show that our system presents sufficient stability under command deformations and head motions. We demonstrate the necessity of collecting such scale of data by gradually reducing training data size. We also validate the generalization ability of our method toward other sensing parameters by reducing the spatial and temporal resolutions. The proof-of-concept design will aim the further development of the commercial products for silent command recognition.
Curiosity-Driven Attention for Anomaly Road Obstacles Segmentation in Autonomous Driving

The inability of semantic segmentation methods to detect anomaly road obstacles not pre-defined in the datasets significantly hinders the safety-critical application in autonomous driving. The excessively complex anomaly detection approaches cannot accommodate the constraints on the inference time of intelligent vehicles. Inspired by the fact that humans have a natural instinct to be curious about unknown objects in a new environment, we propose a novel curiosity-driven attention mechanism (CuDAM) for anomaly road obstacles segmentation. CuDAM adopts the attention map as a new uncertainty judging criterion and utilizes it to improve the efficiency of the model. Specifically, CuDAM is composed of three parts: 1) an attention module for generating an attention map; 2) a reward mechanism for encouraging the network to focus its attention on uncertain regions; 3) an attention loss function for widening the distance between the attention values of deterministic and uncertain pixels. Different from previous approaches, CuDAM can improve both anomaly detection and semantic segmentation performance without complex operations and training, which makes it widely applicable to existing semantic segmentation models. The result of qualitative and quantitative experiments shows that such a straightforward approach achieves consistent significant improvements in anomaly detection performances with the various uncertainty estimation methods, demonstrating the broad applicability of CuDAM.
StARformer: Transformer with State-Action-Reward Representations for Robot Learning

Reinforcement Learning (RL) can be considered as a sequence modeling task, where an agent employs a sequence of past state-action-reward experiences to predict a sequence of future actions. In this work, we propose State-Action-Reward Transformer (StARformer), a Transformer architecture for robot learning with image inputs, which explicitly models short-term state-action-reward representations (StAR-representations), essentially introducing a Markovian-like inductive bias to improve long-term modeling. StARformer first extracts StAR-representations using self-attending patches of image states, action, and reward tokens within a short temporal window. These StAR-representations are combined with pure image state representations, extracted as convolutional features, to perform self-attention over the whole sequence. Our experimental results show that StARformer outperforms the state-of-the-art Transformer-based method on image-based Atari and DeepMind Control Suite benchmarks, under both offline-RL and imitation learning settings. We find that models can benefit from our combination of patch-wise and convolutional image embeddings. StARformer is also more compliant with longer sequences of inputs than the baseline method. Finally, we demonstrate how StARformer can be successfully applied to a real-world robot imitation learning setting via a human-following task.
Clustering Center-based Differential Evolution

In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of the center-based sampling can be utilized at either or both the operation and/or population level. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at operation level requires customizing the strategy for a specific algorithm which degrades the scheme's generalization. In this paper, we have proposed a population-level center-based sampling method which is operation independent and correspondingly can be embedded in any population-based optimization algorithm. In this study, we applied the proposed scheme for Differential Evolution (DE) algorithm to enhance the exploration and exploitation capabilities of the algorithm. We cluster candidate solutions and inject the centroid-based samples into the population to increase the overall quality of the population and thus decrease the risk of premature convergence and stagnation. By a high chance, the center-based samples are effectively generated in the promising regions of the search space. The proposed method has been benchmarked by employing CEC-2017 benchmark test suite on dimensions 30, 50, and 100. The results clearly indicate the superiority of the proposed scheme, and a detailed results analysis is provided.
Random Learning Particle Swarm Optimization with Quasi-Newton Exploitation Mechanism

In traditional particle swarm optimization (PSO) algorithm, each particle updates its velocity and position with a learning mechanism based on its personal historical best position and the best population position. The learning mechanism in traditional PSO is simple and easy to implement, but it suffers some potential problems, such as being easily trapped in local optimum and insufficient balance. Thus, a novel random learning PSO with improved quasi-Newton exploitation mechanism (RQ-PSO) is proposed. Firstly, to improve the global search ability, a random learning mechanism is proposed through the analysis of PSO based on many kinds of learning mechanisms. Then, the random learning mechanism is effectively integrated into PSO to obtain strong global search ability and avoid falling into local optima. Finally, to keep a better balance between exploration and exploitation, an improved quasi-Newton method with strong exploitation ability is incorporated into RL-PSO. The experimental results on the complex functions from CEC-2013 and CEC-2017 test sets show that RQ-PSO outperforms the state-of-the-art PSO variants.
An Effective Foveated 360° Image Assessment Based on Graph Convolution Network

Virtual reality (VR) has been adopted in various fields such as entertainment, education, healthcare, and the military, due to its ability to provide an immersive experience to users. However, 360° images, one of the main components in VR systems, have bulky sizes and thus require effective transmitting and rendering solutions. One of the potential solutions is to use foveated technologies, that take advantage of the foveation feature of the human eyes. Foveated technologies can significantly reduce the data required for transmission and computation complexity in rendering. However, understanding the impact of foveated 360° images on human quality perception is still limited. This paper addresses the above problems by proposing an accurate machine-learning-based quality assessment model for foveated 360° images. The proposed model is proven to outperform the three cutting-edge machine-learning-based models, which apply deep learning techniques and 25 traditional-metric-based models (or analytical-function-based-models), which utilize analytical functions. It is also expected that our model helps to evaluate and improve 360° content streaming and rendering solutions to further reduce data sizes while ensuring user experience. Also, this model could be used as a building block to construct quality assessment methods for 360° videos, that are reserved for our future work. The source code is available at https://github.com/telagment/FoVGCN.
Towards Improving Simulations of Flows around Spherical Particles Using Genetic Programming

The simulation of particle-laden flows is a crucial task in fluid dynamics, requiring high computational cost owing to the complex interactions between numerous particles. Typically, the flow velocity is described with the equations proposed by Stokes. While there is an analytical solution for the Stokes flows around a single spherical particle, the Stokes flows around many particles are still unsolved. In this paper, we study Genetic Programming (GP) for symbolic regressions to explore the potentials of multi-objective GP in recovering analytical expressions for two and, in the future, N particles. We propose a new GP approach containing building blocks to scale up the problem and provide a new benchmark with 22 cases for this application. To identify the strengths and limitations of GP, we generate fully resolved training data from simulations. We compare the results of our algorithm to the superimposition method and a multi-layer perceptron as two baseline methods. The results show that GP can find comparable and sometimes better solutions with smaller failure rates than the two baseline methods. In addition, the produced solutions by GP are explainable and certain function patterns inline with physical laws can be identified across the benchmark problems.
Evolving Counterfactual Explanations with Particle Swarm Optimization and Differential Evolution

Counterfactual explanations are a popular eXplainable AI technique, used to provide contrastive answers to “what-if” questions. These explanations are consistent with the way that an everyday person will explain an event, and have been shown to satisfy the ‘right to explanation’ of the European data regulations. Despite this, current work to generate counterfactual explanations either makes assumptions about the model being explained or utlises algorithms that perform suboptimally on continuous data. This work presents two novel algorithms to generate counterfactual explanations using Particle Swarm Optimization (PSO) and Differential Evolution (DE). These are shown to provide effective post-hoc explanations that make no assumptions about the underlying model or data structure. In particular, PSO is shown to generate counterfactual explanations that utilise significantly fewer features to generate sparser explanations when compared to previous related work.
Neural Architecture Search for Image Dehazing

Manual design of deep networks require numerous trials and parameter tuning, resulting in inefficient utilization of time, energy, and resources. In this article, we present a neural architecture search (NAS) algorithm—AutoDehaze, to automatically discover effective neural network for single image dehazing. The proposed AutoDehaze algorithm is built on the gradient-based search strategy and hierarchical network-level optimization. We construct a set of search space layouts to reduce memory consumption, avoid the NAS collapse issue, and considerably accelerate the search speed. We propose four search spaces <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{AutoDehaze}_{B}$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{AutoDehaze}_{U1}$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{AutoDehaze}_{U2}$</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{AutoDehaze}_{L}$</tex-math></inline-formula> , which are inspired by the boat-shaped, U-shaped, and lateral connection-based designs. To the best of authors knowledge, this is a first attempt to present an NAS method for dehazing with a variety of network search strategies. We conduct a comprehensive set of experiments on Reside-Standard (SOTS), Reside- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> (SOTS) and Reside- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> (HSTS), D-Hazy, and HazeRD datasets. The architectures discovered by the proposed AutoDehaze quantitatively and qualitatively outperform the existing state-of-the-art approaches. The experiments also show that our models have considerably fewer parameters and runs at a faster inference speed in both CPU and GPU devices.
An evolutionary search algorithm for efficient ResNet-based architectures: a case study on gender recognition

Neural Architecture Search (NAS) is a busy research field growing exponentially in recent years. State-of-the-art deep neural networks usually require a specialist to fine-tune the model to solve a specific problem. NAS research aims to design neural network architectures automatically, thus easing the need for machine learning specialists to spend a lot of effort on hand-crafted attempts. As artificial intelligence applications are becoming ubiquitous, there is also a growing interest in efficient applications that could be deployed to smartphones, smart wearable devices, and other edge devices. Gender recognition in unfiltered images — such as those we find in real-world situations like pictures taken with smartphones and video shots from surveillance cameras — is one of such challenging applications. In this work, we developed an evolutionary NAS algorithm that consistently finds efficient ResNet-based architectures, named RENNAS, which have a good trade-off between classification accuracy and architectural and computational complexities. We demonstrate our algorithm's performance on Adience dataset of unfiltered images for gender recognition.
Using Average-Fitness Based Selection to Combat the Curse of Dimensionality

It is well known that metaheuristics for numerical optimization tend to decrease in performance as dimensionality increases. These effects are commonly referred to as “The Curse of Dimensionality”. An obvious change to search spaces with increasing dimensionality is that their volume grows exponentially, and this has led to large amounts of research on improved exploration. A recent insight is that the shape of attraction basins can also change drastically with increasing dimensionality, and this has led to selection-based approaches to combat the Curse of Dimensionality. Average-Fitness Based Selection is introduced as a means to reduce the selection errors caused by Fitness-Based Selection. Experimental results show that the rate of selection errors grows much more slowly for Average-Fitness Based Selection with Increasing dimensionality.
Double-Assimilation of Prosperity and Destruction Oriented Improved Imperialist Competitive Algorithm with Computational Thinking

Whereas the imperialist competitive algorithm (ICA) shows limited global search ability and be liable to be trapped into local optimum, a double-assimilation of prosperity and destruction oriented improved imperialist competitive algorithm (DPDO-IIC A) is proposed tentatively to overcome inherent defects. The imperialist assimilation and colonial reform strategy are customized purposefully, and a novel population redistribution mechanism is introduced as well. The three improvement measures are supposed to further promote population diversity and searching accuracy. The CEC2017 test set is selected to verify the performance of the DPDO-IICA by the different types of numerical function problems with the different dimensions. Moreover, the DPDO-IICA is compared with the three first-class intelligent optimization algorithms, which have achieved significant rankings in the CEC2017 competition. The comparison shows that the DPDO-IICA has good performances, which is demonstrated by the accuracy and stability. In addition, the proportion of imperialists and colonies is investigated, and it is through the community partitioning and clustering dynamically to enhance the population diversity. In conclusion, the DPDO-IICA can effectively improve the ability of global exploration and avoid premature convergence in comparison with the original ICA.
A Grammar-based Evolutionary Approach for Assessing Deep Neural Source Code Classifiers

Neural networks for source code processing have proven to be effective for solving multiple tasks, such as locating bugs or detecting vulnerabilities. In this paper, we propose an evolutionary approach for probing the behaviour of a deep neural source code classifier by generating instances that sample its input space. First, we apply a grammar-based genetic algorithm for evolving Python functions that minimise or maximise the probability of a function to be in a certain class, and we also produce programs that yield an output near to the classification threshold, namely for which the network does not express a clear classification preference. We then use such sets of evolved programs as initial popu-lations for an evolution strategy approach in which we apply, by following different policies, constrained small mutations to the individuals, so to both explore the decision boundary of the network and to identify the features that most contribute to a particular prediction. We furtherly point out how our approach can be effectively used for several tasks in the scope of the interpretable machine learning, such as for producing adversarial examples able to deceive a network, for identifying the most salient features, and further for characterising the abstract concepts learned by a neural model.
The $(1+(\lambda,\lambda))$ Genetic Algorithm on the Vertex Cover Problem: Crossover Helps Leaving Plateaus

Many discrete optimization problems feature plateaus, which are hard to evolutionary algorithms due to the lack of fitness guidance. While higher mutation rates may assist in making a jump from the plateau to some better search point, an algorithm typically performs random walks on a plateau, possibly with some assistance from diversity mechanisms. The vertex cover problem is one of the important NP-hard problems. We found that the recently proposed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1+(\lambda, \lambda))$</tex> genetic algorithm solves certain instances of this problem, including those that are hard to heuristic solvers, much faster than simpler mutation-only evolutionary algorithms. Our theoretical analysis shows that there exists an intricate interplay between the problem structure and the way crossovers are used. It results in a drift towards the points where finding the next improvement is much easier. While this condition is formally proven only on one class of instances and for a subset of search points, experiments show that it is responsible for performance improvements in a much larger range of cases.
Evolving Hardware by Direct Bitstream Manipulation of a Modern FPGA

Field-programmable gate arrays (FPGAs) have re-cently received renewed attention in the context of Evolvable Hardware (EHW). The most fine grained approach to changing their internal structure, direct manipulation of the bitstream, has largely been abandoned. The undocumented bitstream formats of modern FPGAs made it complicated and error-prone. This situation has fundamentally changed with the advent of open-source FPGA toolchains. Previous attempts to exploit this opportunity were promising, but only manged to solve very basic tasks. We present in this paper an evolved tone discriminator circuit. It was evolved by replicating the most famous experiment in this field, but with modern hardware. For that we map the originally used Xilinx XC6200 FPGA to a modern Lattice iCE40 FPGA. We show how to set up the experiment and optimize the evolution environment. Our approach allows over 130 times more reconfigurations per second than previous approaches. Additionally, we discuss reasons for the abandonment of direct bitstream manipulation for EHW in context of the new possibilities created by open-source FPGA toolchains. We show which challenges have been solved and which steps need to be taken next.
Mixed Media in Evolutionary Art

Mixed media in the real world involves the creation of works of art that creatively combine a variety of media on the canvas, for example, watercolour, acrylic paint, and photographs. We present an evolutionary art system that implements a digital version of mixed media. A genetic programming system uses a language that renders different digital effects on a canvas. Each rendered effect takes the form of an “art object”, and the tree defines a s et o fa rt o bjects that together comprise a final rendered image. Available effects include procedural images (textures), image filters, and bitmaps. A n art o bject is rendered onto the canvas via a pre-defined mask shape, which c an range from simple geometric shapes such as circles or squares, to com-plex paintbrush strokes and paint splatters. Fitness evaluation measures the pixel-by-pixel colour distance between a rendered canvas and an input target image, which acts as a compositional guide for rendered images. Various runs of the system have produced an interesting variety of stylized, mixed-effect results, often appearing as abstract “glitchy” interpretations of target images.