Reviewerly/exHarmony huggingface dataset:
Co-design as a Networked Approach to Designing Educational Futures

Abstract Design is a contested term, and this has implications for designing educational futures. Described through five senses to elucidate its complexity, design can be conceptualised as domain, as process, as plan, as the resulting product, and as the intentional creation of new possibilities. In this paper, we use the five design senses to illustrate how they could be useful for making sense of a large, complex, and multifaceted educational project. We define the design senses as a network of socio-material entanglements and illustrate how actor-network theory can be useful in unpacking this network. Taking a postdigital perspective, we illustrate that in designing for learning in higher education, the relationships between the five senses are fluid, constantly shifting, and emergent in a network of human and non-human actants. We argue that design research needs to move beyond cognitive approaches to the study of collaborative approaches that empower participants. In doing so, our study proposes a co-design approach to designing educational futures where multidisciplinary teams connect their knowledge, skills, and resources to carry out a design task. We present three mini-cases from our co-design project to illustrate how the five senses of design can be used to unpack and untangle the web of complex relationships in co-design. Furthermore, we reflect on the shifting role of educational developers as they lead and participate in co-design teams. We conclude by problematising educational design for designing educational futures in a postdigital world.
A Note on Small Weight Codewords of Projective Geometric Codes and on the Smallest Sets of Even Type

.In this paper, we study the codes \(\mathcal C_k(n,q)\) arising from the incidence of points and \(k\)-spaces in \(\textrm{PG}(n,q)\) over the field \(\mathbb F_p\), with \(q = p^h\), \(p\) prime. We classify all codewords of minimum weight of the dual code \(\mathcal C_k(n,q)^\perp\) in case \(q \in \{4,8\}\). This is equivalent to classifying the smallest sets of even type in \(\textrm{PG}(n,q)\) for \(q \in \{4,8\}\). We also provide shorter proofs for some already known results, namely, of the best known lower bound on the minimum weight of \(\mathcal C_k(n,q)^\perp\) for general values of \(q\), and of the classification of all codewords of \(\mathcal C_{n-1}(n,q)\) of weight up to \(2q^{n-1}\).Keywordsprojective geometrycoding theoryminimum weightsets of even typeMSC codes51E2005B2594B05
Anomaly Perception Method of Substation Scene Based on High-Resolution Network and Difficult Sample Mining

The perception of anomalies in power scenarios plays a crucial role in the safe operation and fault prediction of power systems. However, traditional anomaly detection methods face challenges in identifying difficult samples due to the complexity and uneven distribution of power scenarios. This paper proposes a power scene anomaly perception method based on high-resolution networks and difficult sample mining. Firstly, a high-resolution network is introduced as the backbone for feature extraction, enhancing the ability to express fine details in power scenarios and capturing information on small target anomaly regions. Secondly, a strategy for mining difficult samples is employed to focus on learning and handling challenging and hard-to-recognize anomaly samples, thereby improving the overall anomaly detection performance. Lastly, the method incorporates GIOU loss and a flexible non-maximum suppression strategy to better adapt to the varying sizes and dense characteristics of power anomaly targets. This improvement enables higher adaptability in detecting anomalies in power scenarios. Experimental results demonstrate significant improvements in power scene anomaly perception and superior performance in handling challenging samples. This study holds practical value for fault diagnosis and safe operation in power systems.
SandGAN: Style-Mix Assisted Noise Distortion for Imbalanced Conditional Image Synthesis

Conditional Generative Adversarial Networks (CGANs) are well developed on balanced datasets as default standards for generating high-quality images of expected classes. However, the common problem in practice is that training datasets are imbalanced, and there is still a lack of clear conclusions of influences and solutions for imbalanced conditional image synthesis. In this paper, we first introduced a new baseline model by combining state-of-the-art CGANs with resampling and differentiable augmentation to revisit the imbalanced CGANs training. We demonstrate that class-confusion: Synthesized images mixing the attributes belonging to other classes, is the critical factor for low-quality results. To solve this, we proposed Style-Mix Assised Noise Distortion GAN (SandGAN), which explicitly learns style-controllable and style-uncontrollable features to alleviate the effect of data bias though: (i) A learnable selector layer to distort noise by combining features from noise or class-embedding for each class adaptively. (ii) An auxiliary contrastive loss constrains relative distances between sources and style-mix classes in the latent space. The style-mix-based contrastive loss assists in optimizing the distortion weights generated by the selector layer to improve the image quality and keep the intra-class diversity. Finally, experiments on imbalanced CIFAR-10, TinyImageNet and CUB-200 demonstrate that the proposed SandGAN reduces the effect of class confusion caused by data bias and achieves state-of-the-art performance.
Object and attribute recognition for product image with self-supervised learning

Accurate class and attribute recognition is the critical technique to convert the unstructured product image data into structured knowledge base, which provides strong support for product design in the future. However, objects of different classes sharing similar attribute may vary a lot in visual appearances, making it challenging to accurately recognize the objects and their attributes. Different from the traditional multi-label image recognition, the attribute of the object, as high-level semantic information, is not corresponding to certain regions of the object and requires to learn more fine-grained features to represent the latent high level semantic information for the attribute across different object categories. Therefore, a self-supervised method called Deconstruction and Reconstruction Learning Network (DRLN) is proposed to solve the above problems in this paper. The DRLN tries to learn more fine-grained and local feature of the input product image by a self-supervised task, which deconstructs the input product images by randomly shuffling their local regions and further reconstructs the features of corresponding deconstructed images. Besides, the proposed model is optimized in an end-to-end manner by learning from multiple tasks, i.e., the multi-label classification task, the adversarial discrimination task, and the location alignment task. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts for multi-label learning problems on both our product image dataset and another public available attribute recognition dataset. To facilitate future research in this field, all the datasets and codes are directly available online.1
Incremental few-shot learning via implanting and consolidating

This work focuses on tackling the challenging but realistic visual task of Incremental Few-Shot Learning (IFSL), which requires a model to continually learn novel classes from only a few examples while not forgetting the base classes on which it was pre-trained. Our study reveals that the challenges of IFSL lie in both inter-class separation and novel-class representation. Due to intra-class variation, a novel class may implicitly leverage the knowledge from multiple base classes to construct its feature representation. Hence, simply reusing the pre-trained embedding space could lead to a scattered feature distribution and result in category confusion. To address such issues, we propose a two-step learning strategy referred to as Implanting and Compressing (IMCO), which optimizes both feature space partition and novel class reconstruction in a systematic manner. Specifically, in the Implanting step, we propose to mimic the data distribution of novel classes with the assistance of data-abundant base set, so that a model could learn semantically-rich features that are beneficial for discriminating between the base and other unseen classes. In the Consolidating step, we adapt the feature extractor to precisely represent each novel class for enhancing intra-class compactness, together with a regularized parameter updating rule for preventing aggressive model updating. Finally, we demonstrate that IMCO outperforms competing baselines with a significant margin on widely used benchmarks.
A color image decomposition model for image enhancement

The primary goal of image decomposition is to decompose an observed image into several independent components, which can be further manipulated to carry out more complex tasks effectively. Existing image decomposition methods apply a decomposition model either to each of the three channels separately in RGB color space, or to the intensity channel only in other color spaces, which leads to color error or unintuitive display. To address these issues, this paper proposes a color image decomposition model that acts directly on color images and yields visually piecewise smoothing base information, low/high contrast detail information, and rich color information. Specifically, the color information with characteristics of spherical geometry is separated by a spatial transformation at first. Then, the separation of base and detail information from a given image is achieved by combining low-rank approximation and the relative total variation with no artifacts. After yielding the three components via our decomposition model, we exploit the automatic arc tangent transformation and the visual saliency principle to implement two applications, contrast enhancement and infrared image fusion, respectively. We demonstrate the remarkable performance of our method through a reasonable qualitative and quantitative analysis of the experimental results.
An incremental photovoltaic power prediction method considering concept drift and privacy protection

Concept drift (CD) and regional data sharing were considered potential factors critical to affecting data-driven model accuracy. In this paper, an incremental photovoltaic (PV) power prediction method is proposed for addressing the CD of streaming data and the privacy protection of regionally shared data. In the proposed scheme, a novel CD detection method is developed using Energy distance as a virtual drift criterion to measure the data distribution change and prediction error change as a real drift criterion to determine the drift time. In order to incorporate richer data features, this paper jointly trains a PV prediction model based on a broad learning system (BLS) through regional data sharing. Further, an incremental BLS model under the distributed federated learning (FL) framework is designed to maintain the privacy of each PV station's data. Finally, an incremental online model update strategy is proposed to learn new features continuously and without forgetting under the FL framework after CD occurs. The test results on the public dataset from DKASC, Alice Springs PV system, present that the proposed method can effectively detect the CD of streaming data and realize the fast online model update. Compared with the state-of-the-art techniques, the proposed method can significantly improve prediction accuracy while preserving privacy.
Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection

Solar energy supply is usually highly volatile, limiting its integration into the power grid. Accurate probabilistic intraday forecasts of solar resources are essential to increase the share of photovoltaic (PV) energy in the grid and enable cost-efficient balancing of power demand and supply. Solar PV production mainly depends on downwelling surface solar radiation (SSR). By estimating SSR from geostationary satellites, we can cover large areas with high spatial and temporal resolutions, allowing us to track cloud motion. State-of-the-art probabilistic forecasts of solar resources from satellite imagery account only for the advective motion of clouds. They do not consider the evolution of clouds over time, their growth, and dissipation, even though these are major sources of forecast uncertainty. To address the uncertainty of cloudiness evolution, we present SolarSTEPS, the first optical-flow probabilistic model able to simulate the temporal variability of cloudiness. We demonstrate that forecasting the autocorrelated scale-dependent evolution of cloudiness outperforms state-of-the-art probabilistic advection-based forecasts by 9% in continuous ranked probability score (CRPS). This corresponds to an extension of the forecast lead time by about 45 min at constant CRPS. Our work is motivated by the scale-dependent predictability of cloud growth and decay. We demonstrate that cloudiness is more variable in time at smaller spatial scales than at larger ones. Specifically, we show that the temporal autocorrelation of cloudiness is related to its spatial scale by an inverse power law. We also demonstrate that decomposing cloudiness into multiple spatial scales in the forecasts further improves the forecast skill, reducing the CRPS by 10% and the RMSE by 9%.
TEAMSTER: Model-based reinforcement learning for ad hoc teamwork

This paper investigates the use of model-based reinforcement learning in the context of ad hoc teamwork. We introduce a novel approach, named TEAMSTER, where we propose learning both the environment's model and the model of the teammates' behavior separately. Compared to the state-of-the-art PLASTIC algorithms, our results in four different domains from the multi-agent systems literature show that TEAMSTER is more flexible than the PLASTIC-Model, by learning the environment's model instead of assuming a perfect hand-coded model, and more robust/efficient than PLASTIC-Policy, by being able to continuously adapt to newly encountered teams, without implicitly learning a new environment model from scratch.
A robust classification of acute lymphocytic leukemia‐based microscopic images with supervised Hilbert‐Huang transform

Abstract Acute lymphocytic leukemia (ALL) is a malignant condition characterized by the development of blast cells in the bone marrow and their quick dissemination into the bloodstream. It primarily affects children and individuals over the age of 60. Manual blood testing, which has been around for a long time, may be slow. The likelihood of recognizing ALL in its early stages was increased by automating the diagnosis. This research developed an improved criterion for classifying ALL microscopic images into two categories: normal images and blast images. First, to save processing time, innovative image preprocessing techniques were employed to gather data for data augmentation, enhancement, and conversion. The K‐means clustering technique was also utilized to effectively segment the relevant nuclei from the background. Furthermore, the most salient features were extracted using an empirical mode decomposition (EMD) based on the Hilbert‐Huang transform. MATLAB functions such as principal component analysis, gray level co‐occurrence matrix, local binary pattern, shape features, discrete cosine transform, discrete Fourier transform, discrete wavelet transform, and independent component analysis have been used and compared with EMD. The Bayesian regularization (BR) method has been implemented in the neural networks (NNs) classifier. Along with NNs, other classifiers such as support vector machine, K‐nearest neighbors, random forest, naive Bayes, logistic regression, and decision tree have been used, evaluated, and contrasted with NNs. According to experimental findings, the ALL‐IDB2 (Image Database 2) dataset's NNs‐based‐EMD model classified objects with an accuracy of 98.7%, sensitivity of 99.3%, and specificity of 98.1%. Research Highlights Implement a robust method for classifying normal and blast ALL images in the state of the art using the combination of the BR algorithm and the neural networks classifier. Perform robust data processing via data augmentation and conversion from RGB (Red, Green, and Blue) image LAB (Luminosity, A: color space, B: color space) image. Extract the nuclei correctly from the background image using k‐means clustering. Extract the most salient features from the segmented images using EMD in the state of the art of HHT.
Support for digitising the ballot box: A systematic review of i-voting pilots and a conjoint experiment

Governments across the globe have been actively engaged in pilots aimed at implementing i-voting, which facilitates voting via the internet. I-voting innovations, such as those widely institutionalised in Estonia, Canada, and Switzerland, represent a modernising policy innovation that can increase the convenience of electoral participation and can also safeguard against the temporal suspensions of elections, such as those witnessed in several states during the COVID-19 pandemic. Whilst the wider implementation of i-voting has made some process, it has received mixed public support, mostly due to fears over its integrity. In this paper, we ask: what features of i-voting attract public support and inspire trustworthiness in its implementation? We answer this using a pre-registered conjoint experiment fielded in the UK, where we derive attributes from a comprehensive systematic review of existing literature and case studies of real-world implementation. Consistent with a rational-choice model of voting, reforms that are more convenient and have substantive reported benefits, specifically reforms that promise increases in participation, enjoy higher support. Electoral integrity remains an important factor in citizens support for, and trust in, i-voting: across the board, i-voting is perceived as less trustworthy than in-person voting, and proposals that may positively influence the risk of fraud are strongly rejected. Against pre-registered hypotheses, we do not find significant subgroup heterogeneity, for instance regarding satisfaction with internet coverage or the current electoral process, nor do we find significant variation based on past levels of engagement with alternative (convenience) options to in-person voting or indeed partisanship.
Revealing the predictive power of neural operators for strain evolution in digital composites

The demand for high-performance materials, along with advanced synthesis technologies such as additive manufacturing and 3D printing, has spurred the development of hierarchical composites with superior properties. However, computational modelling of such composites using physics-based solvers, while enabling the discovery of optimal microstructures, have prohibitively high computational cost hindering their practical application. To this extent, we show that Neural Operators (NOs) can be used to learn and predict the strain evolution in 2D digital composites. Specifically, we consider three architectures, namely, Fourier NO (FNO), Wavelet NO (WNO), and Multi-wavelet NO (MWT). We demonstrate that by providing a few initial strain frames as input, NOs can accurately predict multiple future time steps in an extremely data-efficient fashion, especially WNO. Further, once trained, NOs forecast the strain trajectories for completely unseen boundary conditions. Among NOs, only FNO offers super-resolution capabilities for estimating strains at multiple length scales, which can provide higher material and pixel-wise resolution. We also show that NOs can generalize to arbitrary geometries with finer domain resolution without the need for additional training. Based on all the results presented, we note that the FNO exhibits the best performance among the NOs, while also giving minimum inference time that is almost three orders magnitude lower than the conventional finite element solutions. Thus, FNOs can be used as a surrogate for accelerated simulation of the strain evolution in complex microstructures toward designing the next composite materials.
Fault diagnosis research of laser gyroscope based on optimized-kernel extreme learning machine

Traditional diagnostic models for laser gyroscopes, widely which are commonly employed as high-precision angular velocity sensors in aerospace applications, often encounter challenges in terms of reliability and accuracy. These challenges arise from difficulties in feature extraction, high computational costs, and lengthy training times. In light of these challenges, the present study proposes a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method utilizes Wavelet Packet Decomposition (WPD) to efficiently extract features from the laser gyroscope signal, which are then used as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis. Afterward, we utilize the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for improved optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches. Additionally, it offers the advantages of shorter training time and increased precision.
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification

Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data. To keep annotation costs low, active graph learning focuses on selecting the most qualitative subset of nodes that maximizes label efficiency. However, deciding which heuristic is best suited for an unlabeled graph to increase label efficiency is a persistent challenge. Existing solutions either neglect aligning the learned model and the sampling method or focus only on limited selection aspects. They are thus sometimes worse or only equally good as random sampling. In this work, we introduce a novel active graph learning approach called DiffusAL, showing significant robustness in diverse settings. Toward better transferability between different graph structures, we combine three independent scoring functions to identify the most informative node samples for labeling in a parameter-free way: i) Model Uncertainty, ii) Diversity Component, and iii) Node Importance computed via graph diffusion heuristics. Most of our calculations for acquisition and training can be pre-processed, making DiffusAL more efficient compared to approaches combining diverse selection criteria and similarly fast as simpler heuristics. Our experiments on various benchmark datasets show that, unlike previous methods, our approach significantly outperforms random selection in 100% of all datasets and labeling budgets tested.