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Audio data augmentation is widely adopted in automatic speech recognition (ASR) to alleviate the overfitting problem. However, noise-based data augmentation converts an over-fitting problem into an under-fitting problem which increases the training time severely. With noise-based data augmentation, informative features are not be persisted during the generating process and generated audio clips would become noise data for the acoustic model. To face the challenge, we propose an Adaptive audio Data Augmentation method called ADA with deep clustering. The proposed ADA could automatically select the most informative augmented sample for each generation. Moreover, two sample selection strategies called RM and RS are proposed. The proposed RM removes samples whose embedding are far away from the cluster center, while the proposed RS maintains the diversity of augmentation samples by sampling in each cluster. Experiments on Aishell-1 demonstrate that the proposed ADA method could improve the data efficiency of end-to-end ASR model in both CNN-based and Transformer-based networks. The proposed ADA obtains an 11.28% and 5.95% relative improvement on SS-CNN and LS-CNN, and a 4.35% improvement on S-Transformer compared with the state-of-the-art audio data augmentation method. Meanwhile, the proposed ADA method decreases the demand of augmented samples by 2.7 times in SS-CNN, LS-CNN and S-Transformer. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed ADA method.

The growing sophistication and resource-intensive nature of deep learning algorithms, especially in the realm of computer vision, have raised significant ecological concerns owing to the high energy demands involved in model training. To address this, we propose the efficient adaptive transformation learning with augmentation framework, which is specifically designed to enhance sustainability within deep learning pipelines. The framework incorporates ongoing power consumption tracking throughout the training process to responsively calibrate the strength of data augmentation and to selectively update layers depending on real-time feedback from both energy and model performance. This adaptive mechanism enables a well-regulated compromise between power efficiency, model precision, and training duration. The framework undergoes extensive benchmarking on three diverse datasets–CIFAR-10, ImageNet, and a tailored satellite imagery corpus–utilizing both legacy convolutional models (ResNet, VGG) and contemporary scalable backbones (ViT-B/16, ConvNeXt-T). EATL-A achieves top-tier accuracy scores of 94.5%, 88.8%, and 90.6%, respectively, surpassing current energy-aware benchmarks like PINN-DT, ssProp, and TinyM2Net-V3 across both performance and F1-score. It further reduces energy consumption by up to 27% and shortens training time by up to 40% relative to standard transfer learning. Additionally, by translating energy metrics into carbon-equivalent emissions, EATL-A is shown to yield the lowest CO2 output, up to 4.6 mg less than the next-best alternative on CIFAR-10. These results underscore EATL-A’s scalability, robustness, and environmental relevance, offering a practical and generalizable framework for sustainable AI across both cloud-based and edge computing platforms.

Training deep learning based handwritten text recognition systems needs a lot of data in terms of text images and their corresponding annotations. One way to deal with this issue is to use data augmentation techniques to increase the amount of training data. Generative Adversarial Networks (GANs) based data augmentation techniques are popular in literature especially in tasks related to images. However, specific challenges need to be addressed in order to effectively use GANs for data augmentation in the domain of text recognition. Text data is inherently imbalanced in terms of frequency of different characters appearing in training samples and the training data as a whole. GANs trained on the imbalanced dataset leads to augmented data that does not represent the minority characters well. In this paper, we present an adaptive data augmentation technique using GANs that deals with the issue of class imbalance arising in text recognition problems. We show, using experimental evaluations on two publicly available datasets for handwritten Arabic text recognition, that the GANs trained using the presented technique is effective in dealing with class imbalanced problem by generating augmented data that is balanced in terms of character frequencies. The resulting text recognition systems trained on the balanced augmented data improves the text recognition accuracy as compared to the systems trained using standard techniques.

Accurate traffic flow forecasting is essential for the stable operation and efficient scheduling of intelligent transportation systems. The key lies in identifying the complex spatio-temporal dependencies within the road network structure. In the real world, traffic data are often noisy and incomplete due to sensor failures, communication interruptions, and other unexpected disturbances. To overcome these challenges, this paper proposes an adaptive spatio-temporal self-supervised traffic flow forecasting method based on contrastive learning (ASTSS-CL). At the graph level, structural perturbations are generated by combining node centrality with nonlinear probabilities, while a learnable temporal-periodic parameter matrix and an attention-based fusion mechanism are introduced to adaptively optimize adjacency relationships. At the temporal level, complementary augmentations are designed in both the time and frequency domains. Dynamic interpolation captures continuous traffic variations, while wavelet decomposition and node-adaptive frequency masking balance low-frequency trends and high-frequency details; random masking further improves robustness to missing observations and disturbances. In addition, spatial heterogeneity learning and contrastive consistency learning are jointly employed to enhance representation quality. Experiments on the PeMS04 and PeMS08 datasets show that ASTSS-CL achieves MAE, RMSE, and MAPE values of 17.95, 28.86, and 12.07% on PeMS04, and 13.78, 22.05, and 9.46% on PeMS08, respectively, outperforming the best-performing baseline. These results validate the effectiveness of the proposed method and demonstrate its potential to support traffic management and the operation of intelligent transportation systems.

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA–ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA–ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

Parkinson disease (PD) is a neurodegenerative disorder which has tremor in the finger, handwriting change and so on. Tremor in the finger with PD changes the typing pattern of subjects. Keystroke dynamics-based PD detection is class-imbalanced problem due to the scarcity of PD keystroke data. To address these problems, we propose a novel multi-level ensemble learning (EL) method that incorporates adaptive data augmentation techniques to diagnose PD. Specifically, we propose adaptive data augmentation methods on three base models to solve class-imbalanced problem. Further, we propose multi-level ensemble learning method for different temporal relation between different types of free-text keystroke dynamics (free-KD). Extensive experiments on datasets demonstrate that accuracy of our proposed method is up to 99.8%. In addition, our proposed method has high generalization and robustness on both free composition and transcription tasks.

Much contemporary rhetoric regards the prospects and pitfalls of using artificial intelligence techniques to automate an increasing range of tasks, especially those once considered the purview of people alone. These accounts are often wildly optimistic, understating outstanding challenges while turning a blind eye to the human labor that undergirds and sustains ostensibly “automated” services. This long-standing focus on purely automated methods unnecessarily cedes a promising design space: one in which computational assistance augments and enriches, rather than replaces, people’s intellectual work. This tension between human agency and machine automation poses vital challenges for design and engineering. In this work, we consider the design of systems that enable rich, adaptive interaction between people and algorithms. We seek to balance the often-complementary strengths and weaknesses of each, while promoting human control and skillful action. We share case studies of interactive systems we have developed in three arenas—data wrangling, exploratory analysis, and natural language translation—that integrate proactive computational support into interactive systems. To improve outcomes and support learning by both people and machines, we describe the use of shared representations of tasks augmented with predictive models of human capabilities and actions. We conclude with a discussion of future prospects and scientific frontiers for intelligence augmentation research.

Corona Virus Disease 2019 (COVID-19) pandemic has increased the importance of Virtual Learning Environments (VLEs) instigating students to study from their homes. Every day a tremendous amount of data is generated when students interact with VLEs to perform different activities and access learning material. To make the generated data useful, it must be processed and managed by the proper machine learning (ML) algorithm. ML algorithms’ applications are many folds with Education Data Mining (EDM) and Learning Analytics (LA) as their major fields. ML algorithms are commonly used to process raw data to discover hidden patterns and construct a model to make future predictions, such as predicting students’ performance, dropouts, engagement, etc . However, in VLE, it is important to select the right and most applicable ML algorithm to give the best performance results. In this study, we aim to improve those ML and DL algorithms’ performance that give an inferior performance in terms of performance, accuracy, precision, recall, and F1 score. Several ML algorithms were applied on Open University Learning Analytics (OULA) dataset to reveal which one offers the best results in terms of performance, accuracy, precision, recall, and F1 score. Two popular ML algorithms called Decision Tree (DT) and Feed-Forward Neural Network (FFNN) provided unsatisfactory results. They were selected and experimented with various techniques such as grid search cross-validation, adaptive boosting, extreme gradient boosting, early stopping, feature engineering, and dropping inactive neurons to improve their performance scores. Moreover, we also determined the feature weights/importance in predicting the students’ study performance, leading to the design and development of the adaptive learning system. The ML techniques and the methods used in this research study can be used by instructors/administrators to optimize learning content and provide informed guidance to students, thus improving their learning experience and making it exciting and adaptive.

Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty. However, posterior computation presents a fundamental barrier to routine use; a single class of algorithms does not work well in all settings and practitioners waste time trying different types of Markov chain Monte Carlo (MCMC) approaches. This article was motivated by an application to quantitative advertising in which we encountered extremely poor computational performance for data augmentation MCMC algorithms but obtained excellent performance for adaptive Metropolis. To obtain a deeper understanding of this behavior, we derive theoretical results on the computational complexity of commonly used data augmentation algorithms and the Random Walk Metropolis algorithm for highly imbalanced binary data. In this regime, our results show computational complexity of Metropolis is logarithmic in sample size, while data augmentation is polynomial in sample size. The root cause of this poor performance of data augmentation is a discrepancy between the rates at which the target density and MCMC step sizes concentrate. Our methods also show that MCMC algorithms that exhibit a similar discrepancy will fail in large samples-a result with substantial practical impact. Supplementary materials for this article are available online.

The reconstruction of the gravity field within the surface region of small bodies is crucial for the surface proximity operations of a probe. However, the irregular shape, uneven mass distribution, and sparse gravitational data of small bodies pose challenges in the reconstruction. We propose a self-adaptive physics-informed neural network (PINN) for the reconstruction of the gravity field within the surface region of irregular and heterogeneous small bodies. First, we introduce an auxiliary-point-based data augmentation strategy to reduce the model’s dependency on the quantity of data. Second, we incorporate a residual-based adaptive sampling strategy to enhance the prediction accuracy of the model in regions with significant variations in small-body density. Finally, we introduce an adaptive weight module based on gradient ascent to mitigate the balancing issue of loss terms in the PINN. Experiments indicate that our algorithm achieves improved accuracy for reconstructing the gravity field within the surface region of small bodies. This work is expected to contribute to the enhancement of safety in surface proximity operations around the surfaces of small bodies.