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With the rapid emergence of the Internet of Things (IoT) devices, there were new vectors for attacking cyber, so there was a need for approachable intrusion detection systems (IDSs) with more innovative custom tactics. The traditional IDS models tend to find difficulties in generalization in the continuously changing and heterogeneous IoT environments. This paper contributes to an adaptive intrusion detection framework using Model-Agnostic Meta-Learning (MAML) and few-shot learning paradigms to quickly adapt to new tasks with little data. The goal of this research is to improve the security of IoT by developing a strong IDS that will perform well across assorted datasets and attack environments. Finally, we apply our proposed framework to two benchmark datasets, UNSW-NB15 and NSL-KDD99, which provide different attack scenarios and network behaviors. The methodology trains a base model with MAML to allow fast adaptation on specific tasks during fine-tuning. Our approach leads to experimental results with 99.98% accuracy, 99.5% precision, 99.0% recall, and 99.4% F1 score on the UNSW-NB15 dataset. The model achieved 99.1% accuracy, 97.3% precision, 98.2% recall, and 98.5% F1 score on the NSL-KDD99 dataset. That shows that MAML can detect many cyber threats in IoT environments. Based on this study, it is concluded that meta-learning-based intrusion detection could help build resilient IoT systems. Future works will move educated meta-learning to a federated setting and deploy it in real time in response to changing threats.

Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field of Deep Meta-Learning advances at great speed, but lacks a unified, in-depth overview of current techniques. With this work, we aim to bridge this gap. After providing the reader with a theoretical foundation, we investigate and summarize key methods, which are categorized into (i) metric-, (ii) model-, and (iii) optimization-based techniques. In addition, we identify the main open challenges, such as performance evaluations on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.

For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data distributions between the old tasks and the new target task impact performance in regression problems. By performing experiments, we discover that these differences greatly affect the relative performance of different adaptation methods. Based on this observation, we develop ensemble schemes combining multiple adaptation methods that can handle a wide range of data distribution differences between the old and new tasks, thus offering more stable performance for a wide range of tasks. For evaluation, we consider three regression problems of sinusoidal fitting, virtual reality motion prediction, and temperature forecasting. The evaluation results demonstrate that the proposed ensemble schemes achieve the best performance among the considered methods in most cases.

Despite the rapid expansion of smart grids and large volumes of data at the individual consumer level, there are still various cases where adequate data collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established Model-Agnostic Meta-Learning algorithm for short-term load forecasting in the context of few-shot learning. Specifically, the proposed method can rapidly adapt and generalize within any unknown load time series of arbitrary length using only minimal training samples. In this context, the meta-learning model learns an optimal set of initial parameters for a base-level learner recurrent neural network. The proposed model is evaluated using a dataset of historical load consumption data from real-world consumers. Despite the examined load series’ short length, it produces accurate forecasts outperforming transfer learning and task-specific machine learning methods by 12.5%. To enhance robustness and fairness during model evaluation, a novel metric, mean average log percentage error, is proposed that alleviates the bias introduced by the commonly used MAPE metric. Finally, a series of studies to evaluate the model’s robustness under different hyperparameters and time series lengths is also conducted, demonstrating that the proposed approach consistently outperforms all other models.

The global education develops competencies and skills necessary to operate in a global environment. These include intercultural communication skills, transnational collaboration, critical thinking and problem solving in a global context, as well as digital and global literacy skills. These skills are essential in the globalized world, where interactions and collaboration with people from various other cultures and countries are commonplace. For example, students and teachers have the opportunity to participate in exchange programs, international partnerships and joint projects, which allow them to understand and connect with other educational systems and cultures. These experiences enrich learning and promote the international cooperation. In this context, the purpose of this work is to carry out a theoretical analysis of the educational dimension in the context of supporting the idea of meta-learning. Such an image involves an epistemic analysis of the concept of \"meta-learning\" from the perspective of strategies for augmenting one’s autonomy in learning. Reported in the school environment, such an approach becomes significant in that we can talk about optimizing the school performance. The meta-learning improves the educational effectiveness and it is defined as a process of awareness on the part of learners of the need to monitor their own learning progress.

Fault diagnosis methods based on deep learning and big data have achieved good results on rotating machinery. However, the conventional deep learning method of bearing fault diagnosis is mostly based on laboratory artificial simulation data, and there is an error with actual fault data, which will reduce the generalization performance of the deep learning method. In addition, labeled data are very precious in real industrial environment. Due to expensive equipment and personnel safety issues, it is difficult to obtain a large amount of high-quality fault labeling data. Therefore, in this paper, we propose a metric-based meta-learning method named Reinforce Relation Network (RRN) for diagnosing bearing faults with few-shot samples. In the proposed method, a 1D convolution neural network is used to extract fault features, and a metric learner is used to predict the similarity between samples under different transfer conditions. Label smoothing and the Adabound algorithm are utilized to further improve the performance of network classification. The performance of the proposed method is verified on a dataset which contains artificial damage and natural damage data. The comparison studies with other methods demonstrate the superiority of the proposed method in the few-shot scenario.

The international organizations of education have already pointed out that the way students learn, what they learn, and the skills needed, will be radically transformed in the coming years. Smart technologies are ready to come into play, changing the conditions of learning, providing opportunities for transformative learning experiences, and promising more conscious, self-directed and self-motivated learning. Meta-learning refers to a set of mental meta-processes by which learners consciously create and manage personal models of learning. Meta-learning entails a cluster of meta-skills that are progressively and hierarchically transformed, ensuring the transition to the highest levels of understanding termed meta-comprehension. The current article aims to investigate the concept of meta-learning and describe the meta-levels of learning through the lens of metacognition. In addition, the potential of smart technologies to provide fertile ground for the implementation of meta-learning training strategies is examined. The results of this article provide a new meta-learning theoretical framework supported by smart devices capable of supporting future meta-learners or, more accurately, meta-thinkers, to transcend the usual states of knowing and move to the next meta-levels of human intelligence.

In the mobile Internet era, recommender systems have become an irreplaceable tool to help users discover useful items, thus alleviating the information overload problem. Recent research on deep neural network (DNN)-based recommender systems have made significant progress in improving prediction accuracy, largely attributed to the widely accessible large-scale user data. Such data is commonly collected from users’ personal devices and then centrally stored in the cloud server to facilitate model training. However, with the rising public concerns on user privacy leakage in online platforms, online users are becoming increasingly anxious over abuses of user privacy. Therefore, it is urgent and beneficial to develop a recommender system that can achieve both high prediction accuracy and strong privacy protection. To this end, we propose a DNN-based recommendation model called PrivRec running on the decentralized federated learning (FL) environment, which ensures that a user’s data is fully retained on her/his personal device while contributing to training an accurate model. On the other hand, to better embrace the data heterogeneity (e.g., users’ data vary in scale and quality significantly) in FL, we innovatively introduce a first-order meta-learning method that enables fast on-device personalization with only a few data points. Furthermore, to defend against potential malicious participants that pose serious security threat to other users, we further develop a user-level differentially private model, namely DP-PrivRec, so attackers are unable to identify any arbitrary user from the trained model. To compensate for the loss by adding noise during model updates, we introduce a two-stage training approach. Finally, we conduct extensive experiments on two large-scale datasets in a simulated FL environment, and the results validate the superiority of both PrivRec and DP-PrivRec.

Deep learning has accomplished impressive excellence in many fields. However, its achievement relies on a vast amount of marker data and when there is insufficient labeled data, the phenomenon of over-fitting will occur. On the other hand, the real world tends to be so non-stationary that neural networks cannot learn continuously like humans. The specific manifestation is that learning new tasks leads to a significant decrease in its performance on old tasks. In responding to the above problem, this paper proposes a new algorithm CMLA (Continual Meta-Learning Algorithm) based on meta-learning. CMLA cannot only extract the key features of the sample, but also optimize the update method of the task gradient by introducing the cosine similarity judgment mechanism. The algorithm is tested on miniImageNet and Fewshot-CIFAR100 (Canadian Institute For Advanced Research), and the outcome clearly reveals the effectiveness and superiority of the CMLA in comparison with other advanced systems. Especially compared to MAML (Model-Agnostic Meta-Learning) with standard four-layer convolution, the accuracy of 1 shot and 5 shot is improved by 15.4% and 16.91% respectively under the setting of 5-way on miniImageNet. CMLA not only reduces the instability of the adaptation process, but also solves the stability-plasticity dilemma to a certain extent, achieving the goal of continual learning.