An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

Towards offering more useful data reliably to mobile cloudfrom wireless sensor network

The integration of ubiquitous wireless sensor network (WSN) and powerful mobile cloud computing (MCC) is a research topic that is attracting growing interest in both academia and industry. In this new paradigm, WSN provides data to the cloud, and mobile users request data from the cloud. To support applications involving WSN-MCC integration, which need to reliably offer data that are more useful to the mobile users from WSN to cloud, this paper first identifies the critical issues that affect the usefulness of sensory data and the reliability of WSN, then proposes a novel WSN-MCC integration scheme named TPSS, which consists of two main parts: 1) TPSDT (Time and Priority based Selective Data Transmission) for WSN gateway to selectively transmit sensory data that are more useful to the cloud, considering the time and priority features of the data requested by the mobile user; 2) PSS (Priority-based Sleep Scheduling) algorithm for WSN to save energy consumption so that it can gather and transmit data in a more reliable way. Analytical and experimental results demonstrate the effectiveness of TPSS in improving usefulness of sensory data and reliability of WSN for WSN-MCC integration

IEEE Access Special Section Editorial : Trust Management in Pervasive Social Networking (TruPSN)

Non peer reviewe

Multicloud-Based Evacuation Services for Emergency Management

A smart evacuation needs a scalable and flexible system to provide service in both emergency and normal situations. A single cloud service is usually limited to support scaling up requirements in an emergency, especially one with a large geographic scope. In this article, the authors propose MCES, a multicloud architecture that deploys smart evacuation services in multiple cloud providers and that can tolerant more pressure than single cloud-based services. This system maintains basic service to support monitoring, but during an emergency, visits to the service will scale up enormously, which means MDSE must support a rapid scaling up of service capacity in a short time. The authors use a three-layer cloud instance management to support rapid capacity scaling in MCES. By conducting extensive simulations, the authors demonstrate that their proposed MCES significantly outperforms single cloud solutions under various emergency settings

Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification

Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes. To alleviate negative effect of noisy edges on neighborhood aggregation, some recent GNNs propose to predict the label agreement between node pairs within a single network. However, predicting the label agreement of edges across different networks has not been investigated yet. Our work makes the pioneering attempt to study a novel problem of cross-network homophilous and heterophilous edge classification (CNHHEC), and proposes a novel domain-adaptive graph attention-supervised network (DGASN) to effectively tackle the CNHHEC problem. Firstly, DGASN adopts multi-head GAT as the GNN encoder, which jointly trains node embeddings and edge embeddings via the node classification and edge classification losses. As a result, label-discriminative embeddings can be obtained to distinguish homophilous edges from heterophilous edges. In addition, DGASN applies direct supervision on graph attention learning based on the observed edge labels from the source network, thus lowering the negative effects of heterophilous edges while enlarging the positive effects of homophilous edges during neighborhood aggregation. To facilitate knowledge transfer across networks, DGASN employs adversarial domain adaptation to mitigate domain divergence. Extensive experiments on real-world benchmark datasets demonstrate that the proposed DGASN achieves the state-of-the-art performance in CNHHEC.Comment: IEEE Transactions on Neural Networks and Learning Systems, 202

Neighbor Contrastive Learning on Learnable Graph Augmentation

Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph datasets. In addition, the contrastive losses originally developed in computer vision have been directly applied to graph data, where the neighboring nodes are regarded as negatives and consequently pushed far apart from the anchor. However, this is contradictory with the homophily assumption of networks that connected nodes often belong to the same class and should be close to each other. In this work, we propose an end-to-end automatic GCL method, named NCLA to apply neighbor contrastive learning on learnable graph augmentation. Several graph augmented views with adaptive topology are automatically learned by the multi-head graph attention mechanism, which can be compatible with various graph datasets without prior domain knowledge. In addition, a neighbor contrastive loss is devised to allow multiple positives per anchor by taking network topology as the supervised signals. Both augmentations and embeddings are learned end-to-end in the proposed NCLA. Extensive experiments on the benchmark datasets demonstrate that NCLA yields the state-of-the-art node classification performance on self-supervised GCL and even exceeds the supervised ones, when the labels are extremely limited. Our code is released at https://github.com/shenxiaocam/NCLA

General Point Model with Autoencoding and Autoregressive

The pre-training architectures of large language models encompass various types, including autoencoding models, autoregressive models, and encoder-decoder models. We posit that any modality can potentially benefit from a large language model, as long as it undergoes vector quantization to become discrete tokens. Inspired by GLM, we propose a General Point Model (GPM) which seamlessly integrates autoencoding and autoregressive tasks in point cloud transformer. This model is versatile, allowing fine-tuning for downstream point cloud representation tasks, as well as unconditional and conditional generation tasks. GPM enhances masked prediction in autoencoding through various forms of mask padding tasks, leading to improved performance in point cloud understanding. Additionally, GPM demonstrates highly competitive results in unconditional point cloud generation tasks, even exhibiting the potential for conditional generation tasks by modifying the input's conditional information. Compared to models like Point-BERT, MaskPoint and PointMAE, our GPM achieves superior performance in point cloud understanding tasks. Furthermore, the integration of autoregressive and autoencoding within the same transformer underscores its versatility across different downstream tasks