Adaptive Learning Framework Research Articles

Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks

In the field of biomedical engineering, predicting Drug-Target Affinities (DTA) is crucial. However, current affinity prediction models are predominantly manually designed, which is a complex, time-consuming process that may not effectively accommodate the diversity and complexity of datasets. To address this challenge, we propose an adaptive learning framework for predicting drug-target affinity, called Adaptive-DTA, which integrates reinforcement learning with graph neural networks to automate the design of affinity prediction models. Adaptive-DTA defines the architecture search space using directed acyclic graphs and employs an reinforcement learning algorithm to guide the architecture search, optimizing parameters based on the entropy of sampled architectures and model performance metrics. Additionally, we enhance efficiency with a two-stage training and validation strategy, incorporating low-fidelity and high-fidelity evaluations. Our framework not only alleviates the challenges associated with manual model design but also significantly improves model performance and generalization. To evaluate the performance of our method, we conducted extensive experiments on DTA benchmark datasets and compared the results with nine state-of-the-art methods. The experimental outcomes demonstrate that our proposed framework outperforms these methods, exhibiting outstanding performance in predicting drug-target affinities. Our innovative approach streamlines the design of affinity prediction model, reduces reliance on manual crafting, and enhances model generalization. Its ability to automatically optimize network architectures represents a major step forward in the automation of computational drug discovery processes.

AWDP-FL: An Adaptive Differential Privacy Federated Learning Framework

Data security and user privacy concerns are receiving increasing attention. Federated learning models based on differential privacy offer a distributed machine learning framework that protects data privacy. However, the noise introduced by the differential privacy mechanism may affect the model’s usability, especially when reasonable gradient clipping is absent. Fluctuations in the gradients can lead to issues like gradient explosion, compromising training stability and potentially leaking privacy. Therefore, gradient clipping has become a crucial method for protecting both model performance and data privacy. To balance privacy protection and model performance, we propose the Adaptive Weight-Based Differential Privacy Federated Learning (AWDP-FL) framework, which processes model gradient parameters at the neural network layer level. First, by designing and recording the change trends of two-layer historical gradient sequences, we analyze and predict gradient variations in the current iteration and calculate the corresponding weight values. Then, based on these weights, we perform adaptive gradient clipping for each data point in each training batch, which is followed by gradient momentum updates based on the third moment. Before uploading the parameters, Gaussian noise is added to protect privacy while maintaining model accuracy. Theoretical analysis and experimental results validate the effectiveness of this framework under strong privacy constraints.

An Adaptive Transfer Learning Framework for Data-Scarce HVAC Power Consumption Forecasting

An Adaptive Transfer Learning Framework for Data-Scarce HVAC Power Consumption Forecasting

An Adaptive Transfer Learning Framework for Functional Classification

In this paper, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer more substantial benefits under certain conditions than another dataset with an identical distribution to the target dataset. This observation renders the commonly-used debiasing step in the parameter-based transfer learning algorithm unnecessary under some circumstances to the classification problem. In particular, we propose two adaptive transfer learning algorithms based on the functional Distance Weighted Discrimination (DWD) classifier for scenarios with and without prior knowledge regarding informative sources. Furthermore, we establish the upper bound on the excess risk of the proposed classifiers, providing the statistical gain via transfer learning mathematically provable. Simulation studies are conducted to thoroughly examine the finite-sample performance of the proposed algorithms. Finally, we implement the proposed method to Beijing air-quality data, and significantly improve the prediction of the PM 2.5 level of a target station by effectively incorporating information from source datasets.

GCMA: An Adaptive Multiagent Reinforcement Learning Framework With Group Communication for Complex and Similar Tasks Coordination

GCMA: An Adaptive Multiagent Reinforcement Learning Framework With Group Communication for Complex and Similar Tasks Coordination

Regionally Adaptive Active Learning Framework for Nuclear Segmentation in Microscopy Image

Regionally Adaptive Active Learning Framework for Nuclear Segmentation in Microscopy Image

Interaction aware and multi-modal distribution for ship trajectory prediction with spatio-temporal crisscross hybrid network

Understanding the interactions between a ship and its surrounding ships enables effective trajectory prediction, which is critical to improving the safe navigation of autonomous ships. The prediction of future trajectories is a very challenging problem due to inherent uncertainties and complex spatiotemporal correlations between different ships. However, existing methods ignore the persistence and cross-domain nature of the influence between ships. To address the above challenges, an adaptive learning framework based on spatio-temporal crisscross hybrid network (STCNet) is proposed, which consists of two parts: spatio-temporal interaction aware and multi-modal trajectory prediction. Modeling temporal-dependent features, spatial interaction features and cross-domain features, and performs adaptive fusion to identify important features and capture all dynamic dependencies. Secondly, most methods only focus on the frequent modes of trajectories and cannot cover the actual paths of limited samples. Therefore, we design an augmented sampling method based on fusion knowledge and graph attention mechanism (KGS) to encourage exploration of trajectories in sparse areas of the sample space, and promote more accurate and reasonable future trajectory prediction. Experiments on the Ningbo-Zhoushan Port sea area dataset show that our method achieves better results than other methods.

The application of effective tracking model in the design of student knowledge education games

As the boost of the economy and the improvement of residents’ living standards, education issues are receiving increasing attention; In response to the issue of rational design of educational games, this study investigates the application of effective tracking models in the design of student knowledge education games. Firstly, this study constructs a personalized knowledge tracking model in view of multi feature fusion; Then, it builds a contextualized narrative education game model integrated with the Adaptive learning framework. The results show that when 40% training set is selected, the accuracy of the personalized knowledge tracking model in view of multi feature fusion is the highest, 0.98, the lowest root mean square error is 0.25, and the lowest Mean absolute error is 0.21; The accuracy of Bayesian knowledge tracking is the lowest, 0.57 in the best case, and the Mean absolute error and Root-mean-square deviation are the highest; When the hidden unit is 16, the area index under the working characteristic curve of the model’s subjects is the highest, around 0.964; The average attention level of students in contextual narrative games reached 4.2245, with a SD of 0.7511; The average of knowledge transfer reached 4.1082, with a SD of 0.7881; The average score of students for the feedback dimension of the research design game is 4.736, and the average score for the learning content dimension is 4.529. In summary, the model possesses excellent application effects in the design of educational games and possesses a promoting influence on the advancement of the education industry.

Learning from Feature and Global Topologies: Adaptive Multi-View Parallel Graph Contrastive Learning

To address the limitations of existing graph contrastive learning methods, which fail to adaptively integrate feature and topological information and struggle to efficiently capture multi-hop information, we propose an adaptive multi-view parallel graph contrastive learning framework (AMPGCL). It is an unsupervised graph representation learning method designed to generate task-agnostic node embeddings. AMPGCL constructs and encodes feature and topological views to mine feature and global topological information. To encode global topological information, we introduce an H-Transformer to decouple multi-hop neighbor aggregations, capturing global topology from node subgraphs. AMPGCL learns embedding consistency among feature, topology, and original graph encodings through a multi-view contrastive loss, generating semantically rich embeddings while avoiding information redundancy. Experiments on nine real datasets demonstrate that AMPGCL consistently outperforms thirteen state-of-the-art graph representation learning models in classification accuracy, whether in homophilous or non-homophilous graphs.

Robust adaptive learning framework for semi-supervised pattern classification

Hessian scatter regularized twin support vector machine (HSR-TSVM) employs hinge loss function and L2-distance metric, which makes it ineffective in dealing with outliers and noise data problems. Aiming to this problem, this paper a novel robust adaptive learning framework CL2,pHSR-TSVM is developed for semi-supervised classification tasks. In CL2,pHSR-TSVM, the generalized adaptive robust loss function Lδ(u) is first innovatively introduced to overcome the problem that hinge loss function is not sensitive to noise and outliers. Intuitively, Lδ(u) can improve the robustness of the model by selecting different robust loss functions for different learning tasks during the learning process via the adaptive parameter δ. Secondly, the robust distance metric capped L2,p-norm is introduced in CL2,pHSR-TSVM to reduce and eliminate the exaggerated influence of L2-distance metric on the learning process of outliers, especially when the outliers are far from the normal data distribution, by setting the appropriate parameters. Furthermore, to improve the computational efficiency of CL2,pHSR-TSVM, the fast CL2,pHSR-TSVM is presented for semi-supervised classification tasks. Finally, two effective algorithms are designed to solve our methods respectively, and the convergence and computational complexity are analyzed theoretically. Experimental results demonstrate the effectiveness and robustness of our methods.

A novel robust adaptive subspace learning framework for dimensionality reduction

A novel robust adaptive subspace learning framework for dimensionality reduction

Unsupervised Test-Time Adaptation Learning for Effective Hyperspectral Image Super-Resolution With Unknown Degeneration.

Fusing a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multi-spectral image has provided an effective way for HSI super-resolution (SR). The key lies on inferring the posteriori of the latent (i.e., HR) HSI using an appropriate image prior and the likelihood determined by the degeneration between the latent HSI and the observed images. However, in scenarios with complex imaging environments and various imaging scenes, the prior of HSIs can be prohibitively complicated and the degeneration is often unknown, which causes it difficult to accurately infer the posteriori of each latent HSI. To tackle this problem, we present an unsupervised test-time adaptation learning (UTAL) framework for HSI SR under unknown degeneration. Instead of directly modeling the complicated image prior, it first implicitly learns a content-agnostic prior shared across different images through supervisedly pre-training a mutual-guiding fusion module on extensive synthetic data. Then, it adapts the shared prior to those private characteristics in the latent HSI for posteriori inference through unsupervisedly learning a self-guiding adaptation module and a degeneration estimation network on two observed images in the test phase. Such a two-stage learning scheme models the complicated image prior in a divide-and-conquer manner, which eases the modeling difficulty and improves the prior accuracy. Moreover, the unknown degeneration can be estimated properly. Both of these two advantages empower us to accurately infer the posteriori of the latent HSI, thereby increasing the generalization performance in real applications. Additionally, in order to further mitigate the over-fitting in coping with more challenging cases (e.g., degenerations in both spectral and spatial domains are unknown) and speed up, we propose to meta-train UTAL on extensive synthetic SR tasks and solve it using an alternative optimization strategy such that UTAL learns to produce good generalization performance in real challenging cases with a small number of gradient descent steps. To verify the efficacy of UTAL, we evaluate it on HSI SR tasks with different unknown degenerations as well as some other HSI restoration tasks (e.g., compressive sensing), and report strong results superior to that of existing competitors.

Machining quality prediction of multi-feature parts using integrated multi-source domain dynamic adaptive transfer learning

Machining quality prediction of multi-feature parts has been a challenging problem because of small dataset and inconsistent quality data distribution with respect to each machining feature. Transfer learning that leverages knowledge of one task and can be repurposed on another task seems a good solution for this purpose. However, traditional transfer learning typically has a single source domain and a target domain, which limits its applications in multi-source scenarios (e.g., multi-feature). To solve this issue, this paper proposes a novel integrated multi-source domain dynamic adaptive transfer learning (IMD-DATL) framework for machining quality prediction of multi-feature part machining systems. Specifically, a domain-sample similarity double matching multi-source domain integration method is designed to construct the integration knowledge transfer from multiple source domains to the target domain. A residual feature extraction network based on sample entropy-dynamic channel double-layer attention structure and a fine-grained transferable feature attention module are designed. These three attentions are used to improve the feature learning ability and the adaptation level to the predicted object in the three dimensions of sample, channel and data feature. Finally, multiple sets of comparative experiments in thin-walled part machining systems confirm the effectiveness and superiority of the proposed method for cross-domain quality prediction. Compared with other traditional transfer learning methods, the MAE, RMSE and Score on average of this method are increased by 5.47 %, 4.59 % and 4.84 %, respectively, compared with other multi-source domain adaptation methods, the MAE, RMSE and Score on average of this method are increased by 7.13 %, 7.37 % and 6.52 %, respectively.

Monitoring hydrodynamic vessel performance by incremental machine learning using in-service data

ABSTRACT An adaptive machine learning framework is established for an implicit determination of the performance degradation of a ship due to marine growth, i.e. biofouling. The framework is applied in a case study considering telemetry data of a cruise ship operating predominantly in the Caribbean Sea. The dataset encompasses seven years including three dry-docking intervals and several in-water cleaning events. The COVID-19 period receives special focus due to the drastic change in the operational profile. A main outcome of the study is a comparison of the derived performance estimate to the corresponding results of the industry standard ISO 19030. Additional aspects of the present study include the use of special regularization techniques for incremental machine learning and the increase of transparency through the implementation of prediction intervals indicating model uncertainty. Overall, it is found that the developed machine learning framework shows good agreement with the industry standard underlining its plausibility.

A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces

Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the performance of TL in MI-BCIs. Approach. We propose a cross-dataset adaptive domain selection transfer learning framework that integrates domain selection, data alignment, and an enhanced common spatial pattern (CSP) algorithm. Our approach uses a huge dataset of 109 subjects as the source domain. We begin by identifying non-BCI illiterate subjects from this huge dataset, then determine the source domain subjects most closely aligned with the target subjects using maximum mean discrepancy. After undergoing Euclidean alignment processing, features are extracted by multiple composite CSP. The final classification is carried out using the support vector machine. Main results. Our findings indicate that the proposed technique outperforms existing methods, achieving classification accuracies of 75.05% and 76.82% in two cross-dataset experiments, respectively. Significance. By reducing the need for extensive training data, yet maintaining high accuracy, our method optimizes the practical implementation of MI-BCIs.

A Domain Adaptive Graph Learning Framework to Early Detection of Emergent Healthcare Misinformation on Social Media

A Domain Adaptive Graph Learning Framework to Early Detection of Emergent Healthcare Misinformation on Social Media

A novel dynamic adaptive ensemble learning framework enhanced by harmony search optimization for mild cognitive impairment detection (Preprint)

A novel dynamic adaptive ensemble learning framework enhanced by harmony search optimization for mild cognitive impairment detection (Preprint)

A multi-agent adaptive deep learning framework for online intrusion detection

The network security analyzers use intrusion detection systems (IDSes) to distinguish malicious traffic from benign ones. The deep learning-based (DL-based) IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and costly signature extraction process. However, this new generation of IDSes still needs to overcome a number of challenges to be employed in practical environments. One of the main issues of an applicable IDS is facing traffic concept drift, which manifests itself as new (i.e. , zero-day) attacks, in addition to the changing behavior of benign users/applications. Furthermore, a practical DL-based IDS needs to be conformed to a distributed (i.e. , multi-sensor) architecture in order to yield more accurate detections, create a collective attack knowledge based on the observations of different sensors, and also handle big data challenges for supporting high throughput networks. This paper proposes a novel multi-agent network intrusion detection framework to address the above shortcomings, considering a more practical scenario (i.e., online adaptable IDSes). This framework employs continual deep anomaly detectors for adapting each agent to the changing attack/benign patterns in its local traffic. In addition, a federated learning approach is proposed for sharing and exchanging local knowledge between different agents. Furthermore, the proposed framework implements sequential packet labeling for each flow, which provides an attack probability score for the flow by gradually observing each flow packet and updating its estimation. We evaluate the proposed framework by employing different deep models (including CNN-based and LSTM-based) over the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. Through extensive evaluations and experiments, we show that the proposed distributed framework is well adapted to the traffic concept drift. More precisely, our results indicate that the CNN-based models are well suited for continually adapting to the traffic concept drift (i.e. , achieving an average detection rate of above 95% while needing just 128 new flows for the updating phase), and the LSTM-based models are a good candidate for sequential packet labeling in practical online IDSes (i.e. , detecting intrusions by just observing their first 15 packets).

Enhancing traffic flow prediction: Dual-branch graph convolutional network with rough data inference and adaptive spatial dependencies

Traffic flow prediction is a significant application of deep learning in spatio-temporal forecasting analysis. Existing research faces challenges that hinder prediction accuracy. One challenge is the inadequate capturing of spatial dependencies in traffic flow due to fixed pre-defined graph structures. Moreover, manually designed prior graphs still have limitations in extracting spatial features. Another challenge is the instability in short-term prediction performance when pre-defined graphs are completely abandoned in favor of parameter training. Additionally, ordinary RNN sequence convolution methods also struggle to capture long-term sequential patterns in large historical traffic data, leading to gradient vanishing or exploding issues. To address these challenges, we proposes a graph convolutional network for traffic flow prediction. We combine an improved prior graph with an adaptive graph to form a dual-branch spatio-temporal neural network. In the first branch, we introduce a time graph based on rough data inference to complement the predefined static graph. In the second branch, we construct an adaptive learning framework that dynamically learns the adjacency matrix and captures global road information. By utilizing enhanced multi-scale gated convolution, we extract spatio-temporal dependencies. Our method surpasses most baseline approaches according to extensive experiments conducted on public datasets.

Adaptive learning framework for learning computational thinking using educational robotics

AbstractComputational thinking (CT) has been promoted worldwide by educational systems and is an essential skill for technological citizens. Various strategies have been planned and developed to help in introducing, improving, and delivering CT. One of the strategies is by creating and developing the supporting tools for CT learning. In this article, educational robotics (ER) is chosen as the focus tool to support CT learning. Each CT and ER has a massive field of study. There are various available reports determining the suitability of CT subject integrated with ER for students' learning. However, all students do not develop similar style of learning and thinking. There is difference in their personal traits. There is a lack of research that designed CT learning through ER specifically based on student's preferences. Besides, it resulted in a challenge to determine the suitability of CT and ER for different kind of preferences. Therefore, this study aimed to develop an adaptive learning (AL) framework for students to deliver learning of CT through ER. The framework consists of three submodels: domain model, student model, and adaptation model. One case study is defined, which is learning the introductory level of CT through ER (CTER). At the end of the study, it can be observed that the AL framework produced positive results in performance and perception for various student categories. It was noted that students utilizing the AL framework had superior understanding of CTER. Individually or collaboratively, all students who applied or did not apply the AL framework in studying the CTER introduction had positive learning outcomes.