Out-of-distribution (OOD) detection plays a critical role in ensuring the robustness of machine learning models in open-world settings. While extensive efforts have been made in vision, language, and graph domains, the challenge of OOD detection in hypergraph-structured data remains unexplored. In this work, we formalize the problem of hypergraph out-of-distribution (HOOD) detection, which aims to identify nodes or hyperedges whose high-order relational contexts differ significantly from those seen during training. We propose HyperGOOD, a unified energy-based detection framework that integrates multi-scale spectral decomposition with structure-aware uncertainty propagation. By preserving both low- and high-frequency signals and diffusing uncertainty across the hypergraph, HyperGOOD effectively captures subtle and relationally entangled anomalies. Experimental results on nine hypergraph datasets demonstrate the effectiveness of our approach, establishing a new foundation for robust hypergraph learning under distributional shifts.

Despite the progress made through deep learning, existing Visual Object Tracking (VOT) frameworks struggle with real-world challenges. Recent approaches incorporate additional modalities like Depth, Thermal Infrared, and Language to enhance the robustness of VOT, particularly with the improvement of the depth sensor precision, facilitating RGB-D tracking. However, current RGB-D trackers often copy RGB tracking paradigms, leading to inefficiency due to two-stream architectures that fail to exploit heterogeneous features, and reliance on simplistic or large-parameter fusion methods. To address these challenges, we propose AMTrack, a one-stream RGB-D tracker leveraging Mamba's linear complexity for simultaneous feature extraction and two-stage cross-modal feature fusion. Our innovation also includes a low-parameter Multimodal Mix Mamba (3M) module, which optimizes deep feature fusion and reduces computational overhead. The advantage of the 3M module stems from our Multimodal State Space Model (MSSM), a multimodal feature interaction component reconstructed based on SSM. Experiments across multiple RGB-D tracking datasets indicate that AMTrack achieves superior performance with lower parameters and memory demands compared to state-of-the-arts.

Multimodal sentiment analysis aims to accurately identify sentiment orientations by integrating information from multiple modalities such as text, audio, and video. However, a key challenge in multimodal fusion is effectively balancing the sufficiency and necessity of information across modalities. Traditional models often fail to qualify and capture this balance due to the presence of noise and redundant information in multimodal data, leading to suboptimal performance in sentiment analysis. To address this issue, we propose a novel multimodal sentiment analysis method calledUCMIB-PNS, which is guided by information bottleneck and probabilistic causality. The method employs anUncertainCross-ModalInformationBottleneck(UCMIB)module to reduce redundant information within modalities and maximize discriminative information. The UCMIB utilizes codebooks to dynamically record the distributions of samples and employs random sampling to conduct uncertain modeling across different modalities. It integrates uncertainty-aware contrastive learning and KL divergence for dynamic comparison and compression of information from different modalities. Moreover, UCMIB-PNS uses differentiableProbability ofNecessity andSufficiency(PNS)estimators to estimate and re-weight the sufficiency and necessity of modalities by constructing several counterfactual scenarios through end-to-end learning. Experiments conducted on four publicly available multimodal sentiment analysis datasets demonstrate that UCMIB-PNS achieves optimal performance on both clean and noisy data. Extended experiments further validate the method's robustness under different types of noise.

The out-of-distribution (OOD) detection on graph-structured data is crucial for deploying graph neural networks securely in open-world scenarios. However, existing methods have overlooked the prevalent scenario of multi-label classification in real-world applications. In this work, we investigate the unexplored issue of OOD detection within multi-label node classification tasks. We propose ML-GOOD, a simple yet sufficient approach that utilizes an energy function to gauge the OOD score for each label. We further develop a strategy for amalgamating multiple label energies, allowing for the comprehensive utilization of label information to tackle the primary challenges encountered in multi-label scenarios. Extensive experimentation conducted on seven diverse sets of real-world multi-label graph datasets, encompassing cross-domain scenarios. The results show that the AUROC of ML-GOOD is improved by 5.26% in intra-domain and 6.54% in cross-domain compared to the previous methods. These empirical validations not only affirm the robustness of our methodology but also illuminate new avenues for further exploration within this burgeoning field of research.

With the growing adoption of Hypergraph Neural Networks (HNNs) to model higher-order relationships in complex data, concerns about their security and robustness have become increasingly important. However, current security research often overlooks the unique structural characteristics of hypergraph models when developing adversarial attack and defense strategies. To address this gap, we demonstrate that hypergraphs are particularly vulnerable to node injection attacks, which align closely with real-world applications. Through empirical analysis, we develop a relatively unnoticeable attack approach by monitoring changes in homophily and leveraging this self-regulating property to enhance stealth. Building on these insights, we introduce HyperNear, i.e., $\underline{N}$ode inj$\underline{E}$ction $\underline{A}$ttacks on hype$\underline{R}$graph neural networks, the first node injection attack framework specifically tailored for HNNs. HyperNear integrates homophily-preserving strategies to optimize both stealth and attack effectiveness. Extensive experiments show that HyperNear achieves excellent performance and generalization, marking the first comprehensive study of injection attacks on hypergraphs. Our code is available at https://github.com/ca1man-2022/HyperNear.

Heterophilic graph neural networks (GNNs) have gained prominence for their ability to learn effective representations in graphs with diverse, attribute-aware relationships. While existing methods leverage attribute inference during message passing to improve performance, they often struggle with challenging heterophilic graphs. This is due to edge distribution shifts introduced by diverse connection patterns, which blur attribute distinctions and undermine message-passing stability. This paper introduces H₂OGNN, a novel framework that reframes edge attribute inference as an out-of-distribution (OOD) detection problem. H₂OGNN introduces a simple yet effective symbolic energy regularization approach for OOD learning, ensuring robust classification boundaries between homophilic and heterophilic edge attributes. This design significantly improves the stability and reliability of GNNs across diverse connectivity patterns. Through theoretical analysis, we show that H₂OGNN addresses the graph denoising problem by going beyond feature smoothing, offering deeper insights into how precise edge attribute identification boosts model performance. Extensive experiments on nine benchmark datasets demonstrate that H₂OGNN not only achieves state-of-the-art performance but also consistently outperforms other heterophilic GNN frameworks, particularly on datasets with high heterophily.

Facial expressions in videos inherently mirror the dynamic nature of real-world facial events. Consequently, facial expression recognition (FER) should employ a dynamic graph-based representation to effectively capture the relational structure of facial expressions rather than relying on conventional grid or sequence methods. However, existing graph-based approaches have their limitations. Frame-level graph methods provide a coarse representation of the facial graph across time and space, while landmark-based graph methods need to introduce additional facial landmarks, resulting in a static graph structure. To address these challenges, we propose spatial-temporal relation-aware dynamic graph convolutional networks (ST-RDGCN). This fine-grained relation modeling approach enables the dynamic modeling of evolving facial expressions in videos through dynamic space-time graphs, eliminating the need for facial landmarks. ST-RDGCN encompasses three graph construction paradigms: dynamic independent space graph, dynamic joint space-time graph, and dynamic cross space-time graph. Furthermore, we propose a relation-aware space-time graph convolution (RSTG-Conv) operator to learn informative spatiotemporal correlations in dynamic space-time graphs. In extensive experimental evaluations, our ST-RDGCN demonstrates state-of-the-art performance on the five popular video-based FER datasets, achieving overall accuracy scores of 99.69%, 91.67%, 56.51%, 69.37%, and 49.03% on the CK+, Oulu-CASIA, AFEW, DFEW, and FERV39k datasets, respectively. In particular, our ST-RDGCN outperforms the current best method by 3.6% in UAR on the most challenging FERV39k dataset. Furthermore, our analysis reveals that the dynamic cross space-time graph scheme is the most effective among the three dynamic graph construction schemes.

Graph anomaly detection (GAD) has attracted increasing interest due to its critical role in diverse real-world applications. Graph neural networks (GNNs) offer a promising avenue for GAD, leveraging their exceptional capacity to model complex graph structures and relationships. However, existing GNN-based models encounter challenges in addressing the GAD's fundamental issue-anomaly camouflage, where anomalies mimic normal instances, leading to indistinguishable features. In this article, we propose a novel approach, termed correlation information enhanced GAD (CIE-GAD). Specifically, drawing on the observation that the distribution of homophilic and heterophilic edges differs between abnormal and normal samples, we construct a hypergraph to learn the co-occurrence relationships among adjacent edges. By enhancing the extraction of sample correlation information, we effectively tackle feature similarity caused by anomaly camouflage, thereby enhancing the performance of GAD. Furthermore, we develop a spectral convolution mechanism based on node-level attention fusion, enabling the capture of multifrequency signals. This module performs adaptive fusion tailored to the unique frequency information requirements of each node, mitigating the local heterophily problem. Extensive experiments on various real-world GAD datasets demonstrate that the proposed CIE-GAD outperforms state-of-the-art methods. Notably, our approach achieves AUC-PR improvements of up to 3.47%, with an average gain of 1.5%, demonstrating its effectiveness in detecting anomalies in graph data.

Multimodal sentiment analysis leverages information from multiple sensors to achieve a comprehensive interpretation of emotions. However, different modalities do not always boost each other as expected. They compete with each other, leading to some modalities being under-optimized during the training process. To address this issue, we propose Adaptive Gradient Scaling with Sparse Mixture-of-Experts (AGS-SMoE) . We first discuss the issue of modal preemption in unified multimodal learning from the perspective of causal preemption. Driven by actual cause, we use the gradient norms from different encoders at two fusion stages as evidence, estimating the current modal preemption state using a parameter-free method. Then, based on the dynamic preemption factor, we design a gradient scaling method to balance optimization for different encoders. Furthermore, we use Mixture-of-Experts to sparsify and perceive multimodal tokens in different preemption states. As a result, our experiments on four multimodal sentiment analysis datasets have achieved state-of-the-art results. Moreover, our method improves modal representation learning at different stages. Extensive experiments confirm that our method can alleviate the modal preemption problem in a plug-and-play manner. Our code is available at https://github.com/TheShy-Dream/AGS-SMoE .

In online learning, personalized course recommendations that align with learners’ preferences and future needs are essential. Thus, the development of efficient recommender systems is crucial to guide learners to appropriate courses. Graph learning in recommender systems has been extensively studied, yet many models focus on low-frequency information, underscoring similar learner preferences and overlooking high-frequency data that indicates varied learning trajectories. Furthermore, course co-occurrence and sequential relationships are often insufficiently investigated. In this paper, we introduceEduGraph, a novel framework developed specifically for MOOC course recommendation systems.EduGraphis characterized by its incorporation of a learning path-based hypergraph, a unique perspective wherein learners are represented as hyperedges, and courses are delineated as vertices. The framework incorporates a framelet-based hypergraph convolution, integrating low-pass filters to highlight similarities and high-pass filters to underscore distinct learning paths among learners. Furthermore,EduGraphfeatures a dual hypergraph learning model, with channels designated for vertex and hyperedge encoding, fostering a collaborative information exchange that refines the learners’ preference embeddings. The empirical assessment ofEduGraphis conducted through a comprehensive comparison with many existing baselines, utilizing two distinct MOOC datasets. Our experimental studies not only emphasize the enhanced recommendation performance ofEduGraphbut also elucidate the significant contributions of its individual components, such as the integration of low-pass and high-pass filters and the framelet-wise collaborative strategy that effectively bridges hyperedge-level and vertex-level representations, augmenting the overall efficacy of the course recommendation system.

Blended learning networks (BLNs) based on the integration of online learning networks and offline learning environments provide new opportunities and platforms for people to acquire and update useful knowledge and carry out all kinds of learning activities anytime and anywhere. Effective modeling and regulation of the knowledge dissemination process can accurately grasp its dissemination process, promote knowledge innovation and collaborative sharing among learners, and accelerate the maximization of knowledge dissemination. However, it is a challenge to establish a comprehensive dynamics model and adopt the optimal regulation for the knowledge dissemination process under the constraints of a limited budget in large-scale BLNs with diverse learners. To this end, we first explore the evolution process of knowledge dissemination in BLNs and the blended learning interaction process of learners. Based on the system dynamics modeling theory, a dynamics model of knowledge dissemination is established. Second, two kinds of effective regulation strategies are proposed. We establish an optimal regulation system intending to maximize the dissemination of knowledge and use the optimal control theory to tackle the optimal solution distribution of regulation strategies. Then, we propose a knowledge dissemination regulation task allocation method based on the collaborative participation of users, and the reverse auction theory is used to quickly solve the task allocation scheme while ensuring performance. Finally, we demonstrate the effectiveness of proposed models and methods through extensive simulation experiments based on real datasets.

Message passing (MP) is crucial for effective graph neural networks (GNNs). Most local message-passing schemes have been shown to underperform on heterophily graphs due to the perturbation of updated representations caused by local redundant heterophily information. However, our experiment findings indicate that the distribution of heterophily information during MP can be disrupted by disentangling local neighborhoods. This finding can be applied to other GNNs, improving their performance on heterophily graphs in a more flexible manner compared to most heterophily GNNs with complex designs. This article proposes a new type of simple message-passing neural network called Flow2GNN. It uses a two-way flow message-passing scheme to enhance the ability of GNNs by disentangling and redistributing heterophily information in the topology space and the attribute space. Our proposed message-passing scheme consists of two steps in topology space and attribute space. First, we introduce a new disentangled operator with binary elements that disentangle topology information in-flow and out-flow between connected nodes. Second, we use an adaptive aggregation model that adjusts the flow amount between homophily and heterophily attribute information. Furthermore, we rigorously prove that disentangling in message-passing can reduce the generalization gap, offering a deeper understanding of how our model enhances other GNNs. The extensive experiment results show that the proposed model, Flow2GNN, not only outperforms state-of-the-art GNNs, but also helps improve the performance of other commonly used GNNs on heterophily graphs, including GCN, GAT, GCNII, and H2GCN, specifically for GCN, with up to a 25.88% improvement on the Wisconsin dataset.

Deep learning (DL) based knowledge tracing (KT) models have challenges for uninterpretable prediction and parameter representation in educational applications, though they achieved remarkable outcomes in predicting the exercise performance of students. This paper proposes a novel knowledge tracing model of high precision and interpretability (namedXKT) for questions with multiple knowledge concepts based on cognitive learning theories and multidimensional item response theory (MIRT). TheXKTconsists of three differentiable network components: multi-feature embedding, cognition processing network, andMIRT-based neural predictor, which aim to provide an explainable prediction of student exercise performance. Specifically, inXKT, multi-feature embedding learns the rich semantic representation (e.g., knowledge distribution information) to enhance knowledge tracing using a cognition processing network. The cognition processing network performs selective perception, ability memory processing, and long-term knowledge memory processing to ensure the explainable factor representation for theMIRT-based neural predictor. Lastly, theMIRT-based neural predictor employs psychometric parameters to interpret student exercise predictions better. Extensive experiments on four real-world datasets show thatXKToutperforms existingKTmethods in predicting future learner responses. Moreover, ablation studies further show thatXKToffers good interpretability of student performance predictions with multiple knowledge concepts, indicating excellent potential in real-world educational applications.

Three-dimensional point cloud classification is fundamental but still challenging in 3-D vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and high-level intrinsic features together. These two levels of features are critical to improving classification accuracy. To this end, we propose a dual-graph attention convolution network (DGACN). The idea of DGACN is to use two types of graph attention convolution operations with a feedback graph feature fusion mechanism. Specifically, we exploit graph geometric attention convolution to capture low-level extrinsic features in 3-D space. Furthermore, we apply graph embedding attention convolution to learn multiscale low-level extrinsic and high-level intrinsic fused graph features together. Moreover, the points belonging to different parts in real-world 3-D point cloud objects are distinguished, which results in more robust performance for 3-D point cloud classification tasks than other competitive methods, in practice. Our extensive experimental results show that the proposed network achieves state-of-the-art performance on both the synthetic ModelNet40 and real-world ScanObjectNN datasets.

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks, are receiving extensive attention for their powerful capability in learning node representations on graphs. There are various extensions, either in sampling and/or node feature aggregation, to further improve GCNs' performance, scalability and applicability in various domains. Still, there is room for further improvements on learning efficiency because performing batch gradient descent using the full dataset for every training iteration, as unavoidable for training (vanilla) GCNs, is not a viable option for large graphs. The good potential of random features in speeding up the training phase in large-scale problems motivates us to consider carefully whether GCNs with random weights are feasible. To investigate theoretically and empirically this issue, we propose a novel model termed Graph Convolutional Networks with Random Weights (GCN-RW) by revising the convolutional layer with random filters and simultaneously adjusting the learning objective with regularized least squares loss. Theoretical analyses on the model's approximation upper bound, structure complexity, stability and generalization, are provided with rigorous mathematical proofs. The effectiveness and efficiency of GCN-RW are verified on semi-supervised node classification task with several benchmark datasets. Experimental results demonstrate that, in comparison with some state-of-the-art approaches, GCN-RW can achieve better or matched accuracies with less training time cost.

Multiview clustering refers to partition data according to its multiple views, where information from different perspectives can be jointly used in some certain complementary manner to produce more sensible clusters. It is believed that most of the existing multiview clustering methods technically suffer from possibly corrupted data, resulting in a dramatically decreased clustering performance. To overcome this challenge, we propose a multiview spectral clustering method based on robust subspace segmentation in this article. Our proposed algorithm is composed of three modules, that is: 1) the construction of multiple feature matrices from all views; 2) the formulation of a shared low-rank latent matrix by a low rank and sparse decomposition; and 3) the use of the Markov-chain-based spectral clustering method for producing the final clusters. To solve the optimization problem for a low rank and sparse decomposition, we develop an optimization procedure based on the scheme of the augmented Lagrangian method of multipliers. The experimental results on several benchmark datasets indicate that the proposed method outperforms favorably compared to several state-of-the-art multiview clustering techniques.

A protein complex is a group of associated polypeptide chains which plays essential roles in the biological process. Given a graph representing protein-protein interactions (PPI) network, it is critical but non-trivial to detect protein complexes, the subsets of proteins that are tightly coupled, from it. Network embedding is a technique to learn low-dimensional representations of vertices in networks. It has been proved quite useful for community detection in social networks in recent years. However, unlike social networks, PPI network does not contain rich metadata, so that existing network embedding methods cannot fully capture the network structure of PPI to improve the effect of protein complexes detection significantly. We propose a semi-supervised network embedding model by adopting graph convolutional networks to detect densely connected subgraphs effectively. We compare the performance of our model with state-of-the-art approaches on three popular PPI networks with various data sizes and densities. The experimental results show that our approach significantly outperforms other approaches on all three PPI networks.

In the power industry, processing business big data from geographically distributed locations, such as online line-loss analysis, has emerged as an important application. How to achieve highly efficient big data storage to meet the requirements of low latency processing applications is quite challenging. In this paper, we propose a novel adaptive power storage replica management system, named PARMS, based on stochastic configuration networks (SCNs), in which the network traffic and the data center (DC) geodistribution are taken into consideration to improve data real-time processing. First, as a fast learning model with less computation burden and sound prediction performance, the SCN model is employed to estimate the traffic state of power data networks. Then, a series of data replica management algorithms is proposed to lower the effects of limited bandwidths and a fixed underlying infrastructure. Finally, the proposed PARMS is implemented using data-parallel computing frameworks (DCFs) for the power industry. Experiments are carried out in an electric power corporation of 230 million users, China Southern power grid, and the results show that our proposed solution can deal with power big data storage efficiently and the job completion times across geodistributed DCs are reduced by 12.19% on average.

This paper considers a problem of landmark point detection in clothes, which is important and valuable for clothing industry. A novel method for landmark localization has been proposed, which is based on a deep end-to-end architecture using prior of key point associations. With the estimated landmark points as input, a deep network has been proposed to predict clothing categories and attributes. A systematic design of the proposed detecting system is implemented by using deep learning techniques and a large-scale clothes dataset containing 145 000 upper-body clothing images with landmark annotations. Experimental results indicate that clothing categories and attributes can be well classified by using the detected landmark points, which are associated with regions of interest in clothes (e.g., the sleeves and the collars) and share robust learning representation property with respect to large variances of human poses, nonfrontal views, or occlusion. A comprehensive performance evaluation over two newly released datasets is carried out in this paper, showing that the proposed system with deep architecture for clothing landmark detection outperforms the state-of-the-art techniques.

Multi-modal data lies on heterogeneous feature spaces, which brings a significant challenge to cross-modal retrieval. Some works have been proposed to cope with this problem by learning a common subspace. However, previous methods often learn the common subspace by enhancing the relation between embedded features and relevant class labels but ignore the relation between embedded features and irrelevant class labels. Additionally, most methods assume that irrelevant samples are of equal importance. Considering this, we propose to train an optimal common embedding space via cross-modal learning to rank with adaptive listwise constraint (CMAL2R) based on two-branch neural networks. The listwise loss function in CMAL2R adaptively assigns larger margins to harder irrelevant samples, strengthening the relation between embedded features and irrelevant class labels. Experiments on Wikipedia and Pascal datasets demonstrate the effectiveness for bi-directional image-text retrieval.

Graph visualization finds wide applications in different areas. As the popularity of social network sites is increasing, it becomes particularly useful in visual analysis of these sites. A number of algorithms for graph visualization have been developed over the past decades. The issue on how to design and develop algorithms by taking into account the characteristics of real graphs such as scale-free and hierarchical structures, however, has not been well addressed. In this paper, we extend the concept of a node degree to a node global degree for a node in a graph, and present an algorithm that computes their scores of all nodes. By taking advantage of the common structure features of real networks, two scalable extensions of this algorithm are further provided that are able to approximate computation results. Based on node global degrees, a filtering approach is presented to reduce the visual complexity of a layout. Extensive experiments have demonstrated the performance of the proposed algorithms in terms of two common evaluation metrics, as well as visualization results. In addition, we have implemented the algorithms in a prototype system, which enable users to explore a graph at continuous levels of details in real time, as evidenced by several real examples.

Multitask learning (MTL) aims to improve the generalization performance of multiple tasks by exploiting the shared factors among them. Various metrics (e.g., -score, area under the ROC curve) are used to evaluate the performances of MTL methods. Most existing MTL methods try to minimize either the misclassified errors for classification or the mean squared errors for regression. In this paper, we propose a method to directly optimize the evaluation metrics for a large family of MTL problems. The formulation of MTL that directly optimizes evaluation metrics is the combination of two parts: 1) a regularizer defined on the weight matrix over all tasks, in order to capture the relatedness of these tasks and 2) a sum of multiple structured hinge losses, each corresponding to a surrogate of some evaluation metric on one task. This formulation is challenging in optimization because both of its parts are nonsmooth. To tackle this issue, we propose a novel optimization procedure based on the alternating direction scheme of multipliers, where we decompose the whole optimization problem into a subproblem corresponding to the regularizer and another subproblem corresponding to the structured hinge losses. For a large family of MTL problems, the first subproblem has closed-form solutions. To solve the second subproblem, we propose an efficient primal-dual algorithm via coordinate ascent. Extensive evaluation results demonstrate that, in a large family of MTL problems, the proposed MTL method of directly optimization evaluation metrics has superior performance gains against the corresponding baseline methods.

Role assignment is a critical element in the role-based collaboration process. There are many factors to consider when decision makers undertake this task. Such factors include a decision maker's preferences and the team's performance. This paper proposes a series of methods, relative to these factors, to solve the group role assignment with balance problem through an association with the one clause at a time approach that is a well-accepted and logic-based association rule mining method. The proposed methods are verified by simulation experiments. The experimental results present the practicability of the proposed solutions. Using the proposed methods, decision makers need only to establish coarse-grain preferences. The fine-grain preferences can be mined. Furthermore, a balance is obtained between the fine-grain preferences and the team's performance.

Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.

Energy‐efficient data gathering techniques play a crucial role in promoting the development of smart portable devices as well as smart sensor devices based Internet of Things (IoT). For data gathering, different applications require different delay constraints; therefore, a delay Differentiated Services based Data Routing (DSDR) scheme is creatively proposed to improve the delay differentiated services constraint that is missed from previous data gathering studies. The DSDR scheme has three advantages: first, DSDR greatly reduces transmission delay by establishing energy‐efficient routing paths (E2RPs). Multiple E2RPs are established in different locations of the network to forward data, and the duty cycles of nodes on E2RPs are increased to 1, so the data is forwarded by E2RPs without the existence of sleeping delay, which greatly reduces transmission latency. Secondly, DSDR intelligently chooses transmission method according to data urgency: the direct‐forwarding strategy is adopted for delay‐sensitive data to ensure minimum end‐to‐end delay, while wait‐forwarding method is adopted for delay‐tolerant data to perform data fusion for reducing energy consumption. Finally, DSDR make full use of the residual energy and improve the effective energy utilization. The E2RPs are built in the region with adequate residual energy and they are periodically rotated to equalize the energy consumption of the network. A comprehensive performance analysis demonstrates that the DSDR scheme has obvious advantages in improving network performance compared to previous studies: it reduces transmission latency of delay‐sensitive data by 44.31%, reduces transmission latency of delay‐tolerant data by 25.65%, and improves network energy utilization by 30.61%, while also guaranteeing the network lifetime is not lower than previous studies.

To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named “Quality of Information Satisfaction Degree” (QoISD) is to quantify how much collected sensing data can satisfy the application’s QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine‐grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine‐grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.

In wireless sensor networks (WSNs), many applications require a high reliability for the sensing data forwarding to sink. Due to the lossy nature of wireless channels, achieving reliable communication through multihop forwarding can be very challenging. Broadcast technology is an effective way to improve the communication reliability so that the data can be received by multiple receiver nodes. As long as the data of any one of the receiver nodes is transmitted to the sink, the data can be transmitted successfully. In this paper, a cross‐layer optimization protocol named Adaptive transmission Power control based Reliable data Forwarding (APRF) scheme by using broadcast technology is proposed to improve the reliability of network and reduce communication delay. The main contributions of this paper are as follows: (1) for general data aggregation sensor networks, through the theoretical analysis, the energy consumption characteristics of the network are obtained. (2) According to the case that the energy consumption of near‐sink area is high and that in far‐sink area is low, a cross‐layer optimization method is adopted, which can effectively improve the data communication by increasing the transmission power of the remaining energy nodes. (3) Since the reliability of communication is improved by increasing the transmission power of the node, the number of retransmissions of the data packet is reduced, so that the delay of the packet reaching the sink node is reduced. The theoretical and experimental results show that, applying APRF scheme under initial transmission power of 0 dBm, although the lifetime dropped by 13.77%, delay could be reduced by 40.37%, network reliability could be reduced by 10.08%, and volume of data arriving at sink increased by 10.08% compared with retransmission‐only mechanism.

Cancer diseases are caused by combination of genetic, environmental, and lifestyle factors. Therefore, it is difficult for health organization to treat this disease. This study focuses on identifying the landscape of Cancer research by using bibliometric methods and social network analysis methods based on a number of research articles related to Cancer retrieved from PubMed. To deeply understand the landscape of research on this disease, we adopt productivity analysis which consists of author, university/institution, country and frequent MeSH terms analysis. We specifically perform the concept graph-based network analysis by applying four centrality measures and analyzing co-occurrence of MeSH terms. In the end, we propose a method to predict the Rising Star that may be the active researcher in the field of Cancer disease in the next few years. With this method, we can possibly find more academic cooperation via academic social networks. The encouraging results show that our work is highly feasible.

Abstract Cloud computing offers a cheap and efficient solution for the deployment of web applications. It results in a big increase of the number of service provider. Users hold multiple identities for using services from different domains. The openness of public clouds requires the authentication system to accept user identities from various domains and to support hybrid authentication protocols. This work proposes a cross‐domain single sign‐on mechanism to address the preceding issues and makes a formal mathematical model to analyze the security issues of the proposed mechanism's authentication architecture; furthermore, an algorithm is proposed to detect the authentication architecture's weak vertex whose failure would lead to a partial failure in the architecture. The proposed mechanism allows service providers to verify user identities in a decentralized way and allows users to unify their identities from various domains in a safe way. The verification process used in this mechanism is able to support hybrid authentication protocols as well as to accelerate the verification of credentials by eliminating single point of failure and single‐point bottleneck. Copyright © 2015 John Wiley & Sons, Ltd.