Accurate sleep staging is essential for assessing sleep quality and diagnosing sleep disorders, yet it heavily depends on large-scale, expertly labeled datasets, which are costly and time-consuming to produce. While existing methods aim to reduce this reliance, they often utilize data from a limited number of subjects, thereby restricting data diversity and hindering model generalization. To address these challenges, we propose SleepAC, a novel model designed to reduce the dependence on extensive manual annotations. It employs an adaptive sample selection strategy that prioritizes informative and diverse samples, starting with simpler ones and gradually adding more complex ones, while incorporating sleep-specific factors., enabling accurate classification with fewer labeled samples. Furthermore, SleepAC integrates a contrastive learning framework that generates hard negative samples across different sleep stages, effectively enhancing the classification of transitional stages, which are particularly difficult due to limited annotations. Experiments on four public datasets demonstrate that SleepAC achieves competitive accuracy and F1-scores, attaining approximately 95% of the fully supervised performance using only 20% of labeled data. These results underscore its effectiveness in low-resource settings, showcasing promising generalization across complex sleep dynamics while significantly reducing annotation costs.

Graph contrastive learning (GCL) has drawn much research attention for its ability to learn node representations in a self-supervised manner. However, the homophily assumption inherent in GNN encoders limits the direction (macro-level) and the process (micro-level) of message passing in current GCL frameworks, impairing the expressive power of GCL in non-homophilous graphs. This paper presents a novel framework that employs Macro and Micro Message Passing in GCL (M3P-GCL) to overcome these limitations and advance performance in both homophilous and non-homophilous graphs. Specifically, at the macro-level, we integrate structural and attribute views to enhance the direction of message passing, and employ an Aligned Priority-Supporting View Encoding (APS-VE) strategy to facilitate contrastive training; at the micro-level, we propose an Adaptive Self-Propagation (ASP) strategy based on role segmentation of self-loops to diversify the process of message passing in the encoder. These enhancements effectively address the limitations imposed by the homophily assumption. Experiments demonstrate that M3P-GCL outperforms both supervised and unsupervised baselines in the node classification task on various datasets with different levels of homophily.

Spatial transcriptomic data provides gene expression, spatial positions, and histopathological images for each spot. However, due to technical difficulties in obtaining high-quality spatial transcriptomic data, it is crucial to enhance the quality of raw gene expression data through computational methods. Existing methods can not effectively fuse gene features and morphological features, and deep learning-based methods lack interpretability. Based on this, we propose EMST, an interpretable multi-modal model for spatial transcriptomic data analysis. We first extract morphological features of spots using a pre-trained large biological model, and then integrate morphological and gene features through a co-attention-based multi-modal fusion module. The fused features undergo an interpretable contrastive learning model to complete feature learning and enhance original gene expression data. Comparative experimental results on multiple datasets show that EMST can effectively enhance gene expression. We also conducted an interpretability analysis to make the training process of the model clearer.

Graph Contrastive Learning (GCL) is a powerful self-supervised technique for learning node and graph representations. However, real-world graph data often exhibit imbalanced class distributions, which pose significant challenges to GCL’s effectiveness. Our experiments show that current state-of-the-art (SOTA) methods perform poorly under imbalanced settings. To address this, we propose a novel GCL framework called AIGCL for imbalanced node classification. This framework automatically and adaptively balances the node representations learned by GCL. Specifically, we introduce a new data augmentation method that retains more information from minority class nodes during graph augmentation. Additionally, we use an imbalance rate adaptive sampling strategy to balance the data. We also incorporate a Variational Graph Autoencoder (VGAE) with an encoder–decoder structure to pretrain the data and generate high-quality pseudo-labels. Our experiments demonstrate that under imbalanced settings, our model improves classification accuracy by 4 %-12 % compared to baseline models, significantly enhancing the performance of minority class nodes.

Time Series Classification (TSC) is a fundamental task in data mining and often suffers from class imbalance, particularly in real-world applications. Traditional methods often fail to capture high-order intrinsic dependencies among time series, especially when minority class samples are scarce. Effectively mining such associations to improve minority-class representation remains a significant challenge. To address this issue, we propose ITS2Graph, a graph-based generative adversarial learning framework that exploits high-order associations for imbalanced time series classification. An auto-encoder is employed to extract latent representations of time series, based on which pairwise similarities are computed to construct a graph, thereby reformulating TSC as a node classification task. To mitigate class imbalance, a graph generator synthesizes minority-class node features and their topological connections, while a Graph Convolutional Network (GCN) discriminator is trained to distinguish real from generated nodes. Experimental results on 22 real-world time series datasets demonstrate that ITS2Graph outperforms existing algorithms in imbalanced time series classification tasks.

This article studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by neural network. We broadly extract different types of SAR image features and raise the intriguing question whether these extracted features are beneficial to: 1) suppress data variations (e.g., complex land-sea backgrounds, scattered noise) of real-world SAR images and 2) enhance the features of ships that are small objects and have different aspect (length-width) ratios, therefore resulting in the improvement of ship detection. To answer this question, we propose an SAR-ship detection neural network (called 3SD-Net for short), by newly developing bidirectional coordinate attention (BCA) and multiresolution feature fusion (MRF) and a center point distribution module (CPDM) based on CenterNet. In detail, we first develop BCA to make 3SD-Net focus on ship features as much as possible while ignoring the background noise. Second, we leverage MRF to enhance the spatial information of small-scale ships yet solve the nontrivial problem of small-scale and shallower pixels easily lost after deep convolution in SAR images. Moreover, considering the varying length-width ratio of arbitrary ships, we study the probability distribution around the ship center. We concentrate on enhancing the distribution function of the ship center, thereby significantly improving the performance of the basic CenterNet detector. This improvement is achieved without incurring additional computational and time costs. The experimental results obtained from the public SAR-Ship and SSDD datasets demonstrate the superior performance of our method compared with its competitors. Specifically, our 3SD-Net achieves average precision (AP) values of 91.66% and 90.22% on the two datasets, respectively, outperforming YOLOV7 (90.31% and 87.32%) and EfficientVit (90.06% and 90.08%). Source code will be released upon publication.

Attributed network has more network information, so more and more attention is paid to the embedding of attributed network. A few existing works have considered the node attributes plays a crucial role in the quality of network embedding. They use the non-negative matrix factorization (NMF) method to mine the network information of network structure and node attributes respectively. Considering the reconstruction error of NMF method, the original network information will be lost when the final network embedding is generated. In this paper, we propose a novel multi-view attributed network embedding model with manifold regularization (Mane). The manifold regularization is added to the model to better reflect the Riemann geometry structure of the network in the feature space to enhance the information. And the problem of missing information of NMF is solved. Our approach uses the NMF to get the non-negative coefficient matrix corresponding to network structure and node attributes. Then cooperative regularization and manifold regularization is added to obtain more information in the final network embedding. The model proposed in this paper has been verified by experiments on several real data sets. The result shows that the model is superior to the state-of-the-art algorithm in node classification task.

This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network. We broadly extract different types of SAR image features and raise the intriguing question that whether these extracted features are beneficial to (1) suppress data variations (e.g., complex land-sea backgrounds, scattered noise) of real-world SAR images, and (2) enhance the features of ships that are small objects and have different aspect (length-width) ratios, therefore resulting in the improvement of ship detection. To answer this question, we propose a SAR-ship detection neural network (call SAR-ShipNet for short), by newly developing Bidirectional Coordinate Attention (BCA) and Multi-resolution Feature Fusion (MRF) based on CenterNet. Moreover, considering the varying length-width ratio of arbitrary ships, we adopt elliptical Gaussian probability distribution in CenterNet to improve the performance of base detector models. Experimental results on the public SAR-Ship dataset show that our SAR-ShipNet achieves competitive advantages in both speed and accuracy.

KPI anomaly detection plays an important role in operation and maintenance. Due to incomplete or missing labels are common, methods based on VAE (i.e., Variational Auto-Encoder) is widely used. These methods assume that the normal patterns, which is in majority, will be learned, but this assumption is not easy to satisfy since abnormal patterns are inevitably embedded. Existing debias methods merely utilize anomalous labels to eliminate bias in the decoding process, but latent representation generated by the encoder could still be biased and even ill-defined when input KPIs are too abnormal. We propose a model-agnostic causal principle to make the above VAE-based models unbiased. When modifying ELBO (i.e., evidence of lower bound) to utilize anomalous labels, our causal principle indicates that the anomalous labels are confounders between training data and learned representations, leading to the aforementioned bias. Our principle also implements a do-operation to cut off the causal path from anomaly labels to training data. Through do-operation, we can eliminate the anomaly bias in the encoder and reconstruct normal patterns more frequently in the decoder. Our proposed causal improvement on existing VAE-based models, CausalDonut and CausalBagel, improve F1-score up to 5% compared to Donut and Bagel as well as surpassing state-of-the-art supervised and unsupervised models. To empirically prove the debias capability of our method, we also provide a comparison of anomaly scores between the baselines and our models. In addition, the learning process of our principle is interpreted from an entropy perspective.

The continuous development of wireless communication networks makes the interaction between users become frequent, and the analysis of network users has become a hot topic. Attributed network embedding combines user relationship and user attributes in the network to learn high-quality node representations. However, Existing attributed network embedding method ignore the edge information and the information enhancement of node attributes on the edge, so ultimately high-quality node representation cannot be obtained. In order to make full use of node attributes to enhance network edge information, we propose an Attributed Network Embedding via Edge Information Enhancing(ANEE) method. Our method uses random walk based on network structure and node attributes to obtain sequences. After that, In node sequence learning and edge sequence learning these two sequence learning, we adopt Self-Attention mechanism in multiple latent spaces to learn multiple latent correlation information in the sequence, so as to obtain better node representations. In the node sequence learning, the position information in the sequence is added as a constraint, and the node representation with a reasonable position is learned. In edge sequence learning, the attribute relation of adjacent nodes is added to edge sequence relation as additional information to enhance edge information. The performance of model proposed in this paper has been verified by experiments on several real data sets. The result shows that the model is superior to the state-of-the-art algorithm in link prediction task.

Abstract Wireless communication network attack detection is important in the field of wireless communication network data mining. Transfer learning is a powerful tool to solve the problem of link prediction in unlabeled wireless networks. In the feature mapping process of transfer learning, the loss of source domain information is very serious, which leads to the loss of knowledge in inverse mapping. To obtain more specific and complete information in the source domain, a link prediction method based on the distribution function fitting method of domain orthogonal term family is proposed. The key samples in the source domain are used to construct a family of orthogonal polynomials, and the least square approximation of the distribution function is obtained by using the family of orthogonal polynomials. The feature vector set of the key samples is extracted from the source domain, and the pseudo label is assigned to the target training set, so that the link classifier can complete the supervised link prediction task. In addition, an advanced performance of TLAD is obtained by comparing with other state‐of‐the‐art models in practice.

Data are widely collected via the IoT for machine learning tasks in in-home health monitoring applications and mislabeled training data lead to unreliable machine learning models in in-home health monitoring. Researchers have proposed a wide arrangement of algorithms to deal with mislabeled training data, in which one straightforward and effective solution is to directly filter noise from training data so that the negative effects of mislabeled data can be minimized. In essence, noise filtering might be a suboptimal solution because the mislabeled data are not completely useless. The features and distributions of mislabeled data are still useful for learning, especially when training data are insufficient. In this work, we propose a novel framework to learn from mislabeled training data through ambiguous learning (LeMAL). LeMAL mainly consists of two parts. First, it converts the original training data to ambiguous data. Second, an ambiguous learning algorithm is applied to the ambiguous data. In this work, we propose a novel distance-based ambiguous learning algorithm so that the ambiguous data can be used in a better way. Finally, we demonstrate that LeMAL can effectively improve learning performance over existing noise filtering methods.

The data in industrial informatics may be high-dimensional and mislabeled. Irrelevant or noisy features pose a significant challenge to the detection of high-dimensional mislabeling. The traditional method usually adopts a two-step solution, first finding the relevant subspace and then using it for mislabeling detection. This two-step method struggles to provide the optimal mislabeling detection performance, since it separates the procedures of feature selection and label error detection. To solve this problem, in this article, we integrate the two steps and propose a sequential ensemble noise filter (SENF). In the SENF, relevant features are selected and used to generate a noise score for each instance. Continuously, these noise scores guide feature selection in the regression learning. Thus, the SENF falls in the scope of sequential ensemble learning. We evaluate our approach on several benchmark datasets with high dimensionality and much label noise. It is shown that the SENF is significantly better than other existing label noise detection methods.

Network embedding is an important method to learn low-dimensional vector representations of nodes in networks, which has wide-ranging applications in network analysis such as link prediction. Most existing network embedding models focus on the unsigned networks with only positive links. However, networks should have both positive and negative links in practical applications such as the trust and distrust relationships in social networks. It is certain that there are different properties between positive links and negative links, which means the network embedding models designed for unsigned networks are not suitable for signed networks. In this paper, we propose SNE-DML, a signed network embedding model with dynamic metric learning. The model learns positive and negative distance metrics respectively in the training process. We conduct sign prediction experiments on three datasets and compare with seven baselines including three signed network embedding models and four state-of-the-art unsigned network embedding models. The experimental results show the effectiveness of our model.

Personalized healthcare envisions providing customized treatment and management plans to individuals at their doorstep. Key factors to ensure personalized healthcare is to involve with the individual in their daily life activities and process the gathered information to provide recommendations. We identified the mostly exposed domains for gathering chronic disease patients information that includes: clinical, social media, and daily life activities. Clinical data is related to the health-care of the patients while social media, sensory, and wearables data is related to the wellness data of the patients. A framework is required to monitor the health and wellness information of the patients for health and wellness analytics provisioning to the physicians for better decision making. We propose Personalized, Ubiquitous Life-care Decision Support System (PULSE); a state of the art decision support system that helps physicians and patients in life-style management of chronic disease patients such as Diabetes. The proposed approach not only utilizes clinical information but also personalized information by correlation to find hidden information using big data health analytic for improvement of life-care. PULSE provides health analytics by utilizing and processing clinical information of the patient. In the same way, it provides wellness analytics to the patients by using their social, activities, emotions and daily life information. The co-relation between clinical and personalized analytics is performed for better recommendations to the patients. This eventually results in improved life-care and healthy living of the individuals.

A trust-aware recommender system (TARS) is widely used in social media to find useful information. S_Searching is one of the most effective recommender searching mechanisms of TARS. It is based on the scale-freeness of the trust network: a skeleton, which consists of hub nodes of the trust network, is involved in trust propagations. Trusts are first propagated from active users to the skeleton, and then recommenders are searched via the skeleton. One fundamental research issue in S_Searching is to search the skeleton for active users efficiently. Existing methods fully search the trust network to find hub nodes in the skeleton for active users. It has high computational cost. In this paper, we propose a novel iterative deepening-based skeleton searching strategy for S_Searching, in which a depth-limited search is run repeatedly. The depth limit is increased with each iteration until it reaches the maximum allowable trust propagation distance. Simulation results show that the computational complexity of our proposed strategy is much less expensive than that of existing methods.

One fundamental requirement of the trust-aware recommender system (TARS) is to efficiently find as many recommenders as possible for the active users. Existing approaches of TARS choose to search the entire trust network, which have very high computational cost. Though the trust network is the scale-free network, we show via experiments that TARS cannot find satisfactory number of recommenders by directly applying the classical searching mechanism of the scale-free network. This is because it chooses the local highest-degree node at each step of the trust propagation. Since the power of the trust network's degree distribution is not big enough, the selected nodes cannot cover superior number of users. In this paper, we propose an efficient searching mechanism, named S_Searching, for TARS based on the scale-freeness of trust networks: choosing the global highest-degree nodes to construct a Skeleton, and searching the recommenders via this Skeleton. Benefiting from the superior outdegrees of the nodes in the Skeleton, S_Searching can find the recommenders very efficiently. Experimental results show that S_Searching can find almost the same number of recommenders as that of conducting full search, which is much more than that of applying the classical searching mechanism in the scale-free network, while the computational complexity and cost is much less.

Trust-aware recommender system (TARS) suggests the worthwhile information to the users on the basis of trust. Existing works of TARS suffers from the problem that they need extra user efforts to label the trust statements. The authors propose a novel model named iTARS to improve the existing TARS by using the implicit trust networks: instead of using the effort-consuming explicit trust, the easy available user similarity information is used to generate the implicit trusts for TARS. Further analysis shows that the implicit trust network has the small-world topology, which is independent of its dynamics. The rating prediction mechanism of iTARS is based on the small worldness of the implicit trust network: the authors set the maximum trust propagation distance of iTARS approximately equals the average path length of the trust network's corresponding random network. Experimental results show that with the same computational complexity, iTARS is able to improve the existing TARS works with higher rating prediction accuracy and slightly worse rating prediction coverage.

Recent research on ubiquitous computing has introduced a new concept of activity-based computing as a way of thinking about supporting human activities in ubiquitous computing environment. Existing access control approaches such as RBAC, became inappropriate to support this concept because they do not consider human activities. In this paper, we propose Activity-Oriented Access Control (AOAC) model, aiming to support user's activity in ubiquitous environments. We have designed and implemented our initial AOAC system. We also built up a simple scenario in order to illustrate how it supports user activities. The results have shown that AOAC meets our objectives. Also, AOAC it takes approximately 0.26 second to give a response which proves that AOAC is suitable to work in real-time environments.

The performance of supervised learning could be improved when valuable data are selected for training. In this paper, we proposed three data selection methods based on fuzzy C-means algorithm. They are: center-based selection, border-based selection and bin-based selection. In center-based selection, the data with high degree of membership in each cluster are selected for training. In border-based selection, the data around the borders between clusters are selected. In bin-based selection, the data in each cluster are sorted based on their membership degrees. Then for each cluster, the sorted data are divided into bins. Finally, there is one data selected from each bin for training. The effects of them are empirically studied on a set of UCI data sets. Experimental results indicate that bin-based selection could effectively improve the performance of learning compared to randomly selecting training samples.

This paper proposes a novel method for data editing. The goal of data editing in instance-based learning is to remove instances from a training set in order to increase the accuracy of a classifier. To the best of our knowledge, although many diverse data editing methods have been proposed, this is the first work which uses semi-supervised learning for data editing. Wilson editing is a popular data editing technique and we implement our approach based on it. Our approach is termed semi-supervised nearest neighbor editing (SSNNE). Our empirical evaluation using 12 UCI datasets shows that SSNNE outperforms KNN and Wilson editing in terms of generalization ability.

Bardram introduced a new concept of activity-based computing as a way of thinking about supporting human activities in ubiquitous environments. In such environments where users are using a multitude of heterogeneous computing devices, the need for supporting users at the activity level becomes essential. However, without considering basic security issues, it could be rife with vulnerabilities. Security services, like authentication and access control, have to not only guarantee security, privacy, and confidentiality for ubiquitous computing resources, but also support user activities equipped with various devices. In this paper, we present an activity-based security scheme. The proposed scheme aims to enhance security services on mobile devices and facilitate user activities. We also integrate off-the-shell security services like MD5, TEA, Diffie-Hellman key agreement protocol so that it makes the scheme more robust and practically usable. The implementation and sample scenario have shown the requirement satisfactory of the scheme.

As the cornerstone of effective patient-physician relationships in the traditional healthcare infrastructures, trust faces new opportunities and challenges in the ubiquitous healthcare. Ubiquitous healthcare enables the agents acquire more information on trust evaluation through effectively resource sharing. Yet ubiquitous healthcare also lays the agents in a more dynamic and uncertainty environment for the trust evaluations. This paper contributes to develop a distributed trust management for the ubiquitous healthcare. Our trust management infrastructure is responsible for evaluating the trust value and assigning access rights based on the trust value. Based on each agent's confidence of its personal experience on other agents, three naive Bayes classifier based algorithms are introduced for the trust evaluation: the robust experience algorithm, the weak experience algorithm and the no experience algorithm. The simulation results show the feasibility and effectiveness of our trust management in the ubiquitous healthcare.

Reputation system is a way to maintain trust in dynamic environments by collecting, distributing and aggregating feedbacks about the service providers' past behaviors. Most existing reputation systems assume that raters evaluate the ratee by means of numerical values. However, raters sometimes cannot express their judgments with exact numerical values, especially when the raters have uncertain or ambiguous opinions on the ratee. Our paper introduces a novel reputation system based on the methodology of Computing with Words (CW), in which the ratings and reputations of computation are words and propositions drawn from a natural language instead of numerical values. Our reputation system has a sound mathematical basis. At the same time, it is convenient for the raters to express their judgments and simple for the participants to understand the integrated reputation.

Activity recognition is a hot topic in context-aware computing. In activity recognition, machine learning techniques have been widely applied to learn the activity models from labeled activity samples. Since labeling samples requires human's efforts, most existing research in activity recognition focus on refining learning techniques to utilize the costly labeled samples as effectively as possible. However, few of them consider using the costless unlabeled samples to boost learning performance. In this work, we propose a novel semi-supervised learning algorithm named En-Co-training to make use of the unlabeled samples. Our algorithm extends the co- training paradigm by using ensemble method. Experimental results show that En-Co-training is able to utilize the available unlabeled samples to enhance the performance of activity learning with a limited number of labeled samples.