Graph Neural Networks (GNNs) are widely applied on graph-level tasks, such as node classification, link prediction and graph generation. Existing GNNs mostly adopt a message-passing mechanism to aggregate node information with their neighbors, which often makes node information similar after rounds of aggregations and leads to oversmoothing. Although recent works have made improvements by combining different message aggregation methods or introducing semantic encodings as priors, these message-passing based GNNs still fail to combat oversmoothing after multiple iterations of node aggregation. Besides, the feature extraction ability of these methods is restricted because of the graph sparsity that hinders the aggregation of node information. To deal with the above two issues, we propose Neighborhood-based and Label-enhanced Graph Transformer (NLGT), a novel and effective framework for graph learning. Specifically, we present a label-enhanced feature fusion mechanism that integrate the shallow node features and label embeddings as enhanced features. Moreover, we design a neighborhood-based mask attention mechanism to alleviate the negative effects caused by the sparsity of the graph. In the predicting stage, we aggregate the prediction results from multiple sampled sub-graphs and apply voting mechanisms to enhance the accuracy and robustness of our framework. Finally, extensive experiments are conducted on four open benchmark datasets, which demonstrate the effectiveness and robustness of our proposed framework compared with existing state-of-the-art methods.

Document-level relation extraction (RE) aims to extract comprehensive correlations between entities and relations from documents. Most of existing works conduct transfer learning on pre-trained language models (PLMs), which allows for richer contextual representation to improve the performance. However, such PLMs-based methods suffer from incorporating structural knowledge, such as entity-entity interactions. Moreover, current works struggle to infer the implicit relations between entities across different sentences, which results in poor prediction. To deal with the above issues, we propose a novel and effective framework, named DocKS-RAG, which introduces extra structural knowledge and semantic information to further enhance the performance of document-level RE. Specifically, we construct a Document-level Knowledge Graph from the observable documentation data to better capture the structural information between entities and relations. Then, a Sentence-level Semantic Retrieval-Augmented Generation mechanism is designed to consider the similarity in different sentences by retrieving the relevant contextual semantic information. Furthermore, we present a hybrid-prompt tuning method on large language models (LLMs) for specific document-level RE tasks. Finally, extensive experiments conducted on two benchmark datasets demonstrate that our proposed framework enhances all the metrics compared with state-of-the-art methods.

Centralized storage in web-based services poses privacy risks for diverse user data, whereas personalized federated learning (PFL) arnesses the inherent privacy-preserving benefits of FL through distributed collaborative training, avoiding the centralization of sensitive data. Besides, PFL also customizes models to individual client needs, enabling personalized web services on non-independent and identically distributed data across varied environments. Existing PFL techniques often rely on prior knowledge to identify personalization layers, overlooking variable layer sensitivities to heterogeneous data, thus constraining the model's adaptability to diverse statistical distributions and limiting its service effectiveness. To overcome this issue, we introduce FedLD, a layer-wise decoupling method for PFL that precisely captures layer-specific sensitivities to heterogeneous data, enhancing service-oriented model personalization. Firstly, FedLD employs a hypernetwork to evaluate each layer's contribution to performance across varied data distributions, determining the optimal proportion of personalized channels per layer for each client, thus enabling fine-grained, channel-level parameter adjustments tailored to individual service needs. Secondly, knowledge distillation is introduced to enforce consistency between the representations of personalized and shared weights, promoting collaboration between them. Thirdly, an improved aggregation strategy is proposed to enhance collaboration among clients with similar data distributions. Extensive experimental results show that FedLD achieves more effective and generalized results compared to baseline methods across various types of heterogeneous data settings.

Federated Learning (FL) addresses the growing concerns of Internet of Things (IoT) service security and privacy in edge computing environments by enabling collaborative model training without the need to centralize sensitive data. Most existing FL frameworks remain vulnerable to sophisticated threats such as source inference attacks (SIAs), which exploit model updates to infer sensitive information about participating clients, thereby compromising the integrity and security of edge services. To mitigate such attacks and ensure service security and user privacy, various defensive methods, such as RM Learning and RelaxLoss, have been proposed. However, these methods fail to provide effective privacy protection in practical FL scenarios characterized by non-IID data distributions. To address this issue, we propose FedMLU, a novel algorithm designed to counter SIAs effectively. Specifically, FedMLU combines a model alternating update strategy with the RelaxLoss algorithm to minimize the loss discrepancy among samples, thereby reducing the distinguishability exploited by SIAs. Furthermore, distinct soft labels are assigned for training each federated participant model, aiming to decrease the model's prediction confidence and enhance privacy protection. Extensive experiments on synthetic datasets and various real-world datasets demonstrate that our method achieves better defense performance and a more favorable tradeoff between privacy protection and model utility compared to the state-of-the-art RelaxLoss and two popular FL frameworks, particularly in scenarios with data heterogeneity.

With the rapid urbanization in the modern era, smart traffic profiling based on multimodal sources of data has been playing a significant role in ensuring safe travel, reducing traffic congestion and optimizing urban mobility. Most existing methods for traffic profiling on the road level usually utilize single-modality data, i.e., they mainly focus on image processing with deep vision models or auxiliary analysis on the textual data. However, the joint modeling and multimodal fusion of the textual and visual modalities have been rarely studied in road traffic profiling, which largely hinders the accurate prediction or classification of traffic conditions. To address this issue, we propose a novel multimodal learning and fusion framework for road traffic profiling, named TraffiCFUS. Specifically, given the traffic images, our TraffiCFUS framework first introduces Vision Language Models (VLMs) to generate text and then creates tailored prompt instructions for refining this text according to the specific scene requirements of road traffic profiling. Next, we apply the discrete Fourier transform to convert multimodal data from the spatial domain to the frequency domain and perform a cross-modal spectrum transform to filter out irrelevant information for traffic profiling. Furthermore, the processed spatial multimodal data is combined to generate fusion loss and interaction loss with contrastive learning. Finally, extensive experiments on four real-world datasets illustrate superior performance compared with the state-of-the-art approaches.

Graph anomaly detection (GAD), with its ability to accurately identify anomalous patterns in graph data, plays a vital role in areas such as network security, social media platforms, and fraud detection. Graph autoencoder-based methods are widely used for GAD due to their efficiency and effectiveness in capturing complex patterns and learning meaningful representations. However, the above methods are constrained by hardware memory, hindering the detection for large-scale graph data. In this paper, we propose a Memory-Efficient framework for large-scale attributed Graph Anomaly Detection (MEGAD). Specifically, MEGAD first generates node embeddings and then refines them through a lightweight joint optimization model, ensuring minimal memory overhead. The optimized embeddings are subsequently fed into a detector to compute anomaly scores. Extensive experiments demonstrate that our framework achieves comparable accuracy to state-of-the-art methods across multiple datasets while significantly reducing memory consumption on large-scale graphs.

Embedding-based recommender systems rely on historical interactions to model users, which poses challenges for recommending to new users, known as the user cold-start problem. Some approaches incorporate social networks to deduce preferences based on the social circles of cold-start users to solve the problem of sparse features. However, such methods have difficulty distinguishing between superficial correlations and causal relationships in social behaviors, leading to inaccuracies in predicting user preferences. To address the aforementioned issues, we propose the Causal Contrastive Learning Recommendation (C2lRec) framework. Specifically, we causally model the inference of hidden preferences from the feature and historical behavior of warm users and predict user interactions based on such preferences. The counterfactual inference is subsequently performed to intervene and extract interactions from historical behaviors of warm users that influence their preferences, designating as primary causal variables. Additionally, we utilize the primary causal variables from users within the social circle of cold-start users to substitute the missing historical interactions of cold-start users and employ a similar causal modeling approach to uncover hidden preferences as we do with warm users. Finally, we realize causal contrastive learning to enhance the distribution of cold-start users. Extensive experiments conducted on three public datasets demonstrate that the recommendation performance of C2lRec exceeds that of state-of-the-art methods.

Dissolved Oxygen (DO) is an important indicator of water quality and sea-air interactions, and the formation mechanism and dramatic change of Oxygen Minimum Zones (OMZs) are closely related to the activities of marine organisms and marine ecological events. However, due to the sparsity and scarcity of DO data, the studies on OMZs get a lot of limits. This paper proposes a deep learning method called SE-LeNet to reconstruct underwater multilayer DO, which integrates the channel attention module called SE-Block into the LeNet model. Simultaneously, the relationship between sea surface elements (e.g. spCO2, PhyC) and DO are created based on CEMES reanalysis data, which has never been considered in the reconstructed model before. Extensive experiments are conducted, demonstrating that the deep learning methods notably outperform the traditional statistical and other machine learning methods when processing high-resolution multi-dimensional data with spatial information. Moreover, the result accuracy is improved after adding the SEBlock module, and the R2and RMSE of SE-LeNet are as low as 0.97 and 12.00 μmol/kg, respectively. SE-LeNet can be successfully used in reconstructing DO data and then promoting the studies on OMZs in the ocean field.

Wave data mining and processing are important in ocean prediction. However, wave data often exhibits imbalance, resulting in low accuracy in predicting extreme phenomena. To alleviate this problem, we propose a hybrid feature selection method based on imbalance learning (HFS-IL) to improve accuracy of prediction models. Specifically, we first use a Long Short-Term Memory (LSTM) network to train an imbalance discriminator, which aims to classify input data into common and rare subsets. Secondly, we select the optimal feature subsets by a hybrid feature selection algorithm, which is innovatively designed by combining mutual information and forward selection. To verify the effectiveness of HFS-IL, we process an imbalanced wave dataset from ERA5 by HFS-IL and use the processed data as input for an intelligent prediction model. The experimental results demonstrate that HFS-IL can effectively alleviate the impact of data imbalance and improve the accuracy of prediction, especially at station 51000, the majority of metrics outperform GRU and OSP-FEAN.

Sea ice cannot only have a significant impact on hydrological changes, climate systems, and energy balance on Earth, but it can also directly interfere with maritime activities, posing serious obstacles to ocean economic development, polar scientific research, and other activities. In today’s world, where global warming is accelerating the melting of polar sea ice, the ability to accurately identify and classify sea ice becomes particularly important. Compared with traditional statistical methods, sea ice classification methods based on machine learning and deep learning have the advantages of fast computing speed and low resource consumption. These methods require a large amount of labeled data as a driver. However, the direct labeling cost of Synthetic Aperture Radar (SAR) sea ice image datasets is high, and previous sea ice classification methods struggle to balance data processing costs with classification accuracy. To address this problem, this paper proposes a sea ice classification method based on textural features and random forest, which is called SIC-TFRF. Specifically, we first extract the textural features based on the gray-level co-occurrence matrix (GLCM) and then use the wrapper method to filter and integrate them into the polarization features of the SAR images, guiding the training of the random forest model. Extensive experiments are conducted to verify the superiority of our proposals. Particularly, the accuracy of the binary classification of sea ice and seawater and the multi-classification of different types of sea ice and seawater can reach 96.51% and 92.78%, respectively.

As an effective knowledge organizing and modeling technique, knowledge graph has become a key topic in graph research, but the practical application of KG is limited by its incompleteness. In recent years, many knowledge graph embedding(KGE) methods for knowledge graph completion(KGC) based on graph neural networks(GNN) have been proposed. However, most GNN-based KGC models are still suffer from the encoder-decoder structure of low efficiency in aggregating neighborhood information and the difficulty of model training. This paper present an optimized model that incorporates Neighborhood information into knowledge inference, to improve the performance of KGC models based on multilayer perceptron network(MLP), which is named N-MlpE. We generate an input sequence that includes the query triplet and its neighbor entities and relationships, and then feed it to an adaptive filter module to remove useless neighbors for the inference to improve the accuracy of the inference, and reduce the computational complexity of training the model. The filtered sequence is then fed into a weight calculation module and a feature extraction module simultaneously, the former is designed based on selfattention to model the relevant rule inference, which enhances the interpretability of KGE models, and the latter is based on MLP and used to capture the long-distance interactions between triplets, which can significantly improve the accuracy of inference. Extensive experiments are conducted on two standard KG datasets WN18RR and FB15k237 to verify the effectiveness of N-MlpE, the results show that the accuracy of N-MlpE model outperforms most GNN-based models.

Underwater acoustic signal denoising is a challenging task due to the complexity of the underwater environment. Most of the existing methods cannot effectively cope with the problem of underwater acoustic signal (UWAS) denoising at low signal-to-noise ratios (SNRs). According to the characteristics of UWAS, a novel idea is proposed to simultaneously model latent features from both the time and frequency dimensions of complex-valued spectrum in a dual-branch self-attention network, namely DBSA-Net. In this model, both magnitude and phase information in the complex spectrum are enhanced from different dimensions by two branches. Specifically, DBSA-Net is an encoder-decoder based network with several global-local-self-attention (GL-SA) blocks distributed on dual branches between encoder and decoder. Each GL-SA block incorporates global self-attention and local self-attention to capture distant context and fine-grained local dependencies along the temporal and frequency dimensions. Moreover, we also design an information interaction module between two branches to exchange complementary information. This interaction module together with a merge block fuse features extracted from different dimensions, thus enhancing the capability of our model to learn the target signal features. Extensive experiments are conducted to evaluate our model on a publicly available dataset. Results of the ablation experiments show that the different modules of DBSA-Net play their respective roles in improving denoising performance and are empirically valid. In both the seen ships and unseen ships scenarios, the proposed DBSA-Net outperforms existing approaches by a large margin on various evaluation metrics.

Underwater acoustic signal recognition systems face challenges in achieving high accuracy when processing complex data with low signal-to-noise ratio (SNR) in underwater environments, leading to limited noise robustness. Conventional approaches typically employ pre-trained denoising models for preprocessing noisy signals. However, due to disparate optimization goals between denoising and recognition models, denoising methods might introduce signal distortion, hampering effective enhancement of system accuracy. To address this issue, this paper proposes a novel joint training framework with cross-attention fusion for robust underwater acoustic signal recognition (UASR), called CAF-JT. CAF-JT consists of a denoising module, a recognition module, and the CAF module. It addresses the mismatch problem arising from different optimization directions by jointly training the denoising frontend and the recognition backend. Additionally, inspired by the multi-condition training (MCT) method, the CAF module is designed to fuse characteristics from both denoised and noisy audio, thus incorporating noise information. This fusion mechanism enables the model to better adapt to the characteristics of the noisy environment and enhance its noise robustness. Furthermore, to improve the performance of UASR, TF-Transformer blocks are incorporated into both the denoising module and the recognition module to capture the spatio-temporal distribution of spectral features. The proposed approach is evaluated on two open-source underwater acoustic signal datasets, namely ShipsEar and DeepShip. Extensive experimental demonstrate the superiority of CAF-JT over conventional joint training approaches, showcasing its improved noise robustness. Particularly in low SNR conditions, CAF-JT achieves the best average recognition rates of 94.84% and 93.61% on the two datasets, respectively.

Underwater acoustic signal denoising technology aims to overcome the challenge of recovering valuable ship target signals from noisy audios by suppressing underwater background noise. Traditional statistical-based denoising techniques are difficult to be applied effectively in complex underwater environments, especially in the case of extremely low signal-to-noise ratios (SNRs). To address these problems, we propose a noise-aware deep learning model with fullband-subband attention network (NAFSA-Net) for underwater acoustic signal denoising. NAFSA-Net adopts an encoder to extract the feature representation of the input audio. Subsequently, the noise subnet and the target subnet are designed to estimate the noise component and the target component simultaneously. Specifically, some stacked fullband-subband attention (FSA) blocks are deployed in each subnet to capture both global dependencies and fine-grained local dependencies of features. Furthermore, we introduce an interaction module to transmit auxiliary information from the noise subnet to the target subnet. Finally, we propose an improved weight SI-SNR loss function to optimize the training of our model. Experimental results show that our proposed NAFSA-Net substantially outperforms traditional methods and competitive DNN-based solutions in denoising underwater noisy signals with very low SNRs. More importantly, our proposals achieve equally excellent performance on both unseen datasets, which indicates that NAFSA-Net can be a more robust choice for real-world underwater acoustic denoising systems.

Dimensionality reduction is a fundamental technique to address the curse of dimensionality problem in real-world big datasets. However, most existing methods either only target raw datasets that contain explicit relationships between data points, or construct the complete neighborhood graph of the dataset by calculating pairwise similarities, and then generate contexts of data points by random walking to measure the structure of the dataset, which are computationally expensive. In this paper, we propose a fast nonlinear locality-preserving dimensionality reduction approach called FVec2vec, which extends the Skip-gram model to embedding representation of general numerical matrices. Specifically, instead of constructing neighborhood graph by calculating pairwise similarities between data points, we approximate the k-nearest neighbors (kNN) of each data point in matrices by exploring its neighbors’ neighbors first. Then, we design a novel sampling algorithm to randomly sample on the kNN to depict the structure of the dataset. Experimental results show that FVec2vec is faster than most existing methods while achieving acceptable accuracy, and the accuracy is even higher than the state-of-the-art method under certain similarity metrics.

Sea Surface Temperature (SST) prediction has attracted increasing attention due to its critical role in climate change. Traditional SST prediction methods can be mainly divided into two types, the physics-based numerical methods and the data-driven methods. However, the above methods have certain limitations, the former type can not perform well when the physical prior information is incomplete, while latter type can not perform well when the training data is insufficient. This paper uses a deep neural network to extract some valuable information from the data, and then introduces the space-time partial differential equation (PDE) to model the prior physical information referring to SST. By incorporating them together, a new Space-Time PDE-guided Neural Network (STPDE-NET), which can better deal with the prior physical information incompleteness and data insufficiency problems mentioned above is proposed. In the experiments, we compare our STPDE-NET with several famous or state-of-the-art SST prediction methods. The experimental results show that STPDE-NET outperforms the compared methods in most SST prediction circumstances, especially when the training data is insufficient.

Accurately forecasting significant wave height (SWH) is meaningful since SWH is an essential parameter in coastal and ocean engineering. In order to accurately predict SWH, we propose the OSP-FEAN method, which optimizes significant wave height prediction by feature engineering and attention network. Specifically, we conduct feature engineering by adding the first-order to twelfth-order lag variables of SWH to the input set for feature enhancement and using the random forest algorithm for feature selection. Moreover, we construct a sequence to sequence neural network. In order to improve the forecast accuracy, we add an attention mechanism based on the memory layer to this neural network. Finally, extensive experiments with observed data at different stations are conducted to verify the effectiveness of our method on 6-h, 12-h and 24-h predictions, especially the superiority in outlier prediction.

Predicting the Arctic sea ice concentration (SIC) has an essential guiding role in understanding climate change trends, resource extraction and route planning. Existing deep learning models still have the problem that it is challenging to utilize the global spatial information of SIC, and the predictions of boundary regions are not accurate enough. In this paper, we propose a new deep learning model, namely ConvLSTM-CRF, to predict the monthly sea ice concentration in the Arctic. We add a dense conditional random fields to the ConvLSTM, which further extracts global spatial information and can predict SIC more accurately. The experimental results show that compared with ConvLSTM, our model has a great improvement in the overall prediction accuracy and has more accurate predictions in the SIC boundary region, especially in the melting and freezing seasons when the SIC changes drastically. Our model also shows better prediction performance when making iterative predictions. In addition, ConvLSTM-CRF can be applied to similar time series forecasting problems, such as precipitation forecasting and snowfall forecasting.

Summary Many cyber‐attach schemes and coding models established by algebra tools are build to address the problem of security of cyber‐pysical systems (CPS). As an important field of algebra computing, Boolean Polynomial System Solving (PoSSo) problem plays a very important role in many algebra applications. In this article, we propose an efficient Parallel Boolean Characteristic Set method (PBCS) under the high‐performance computing environment to improve the efficiency of solving Boolean polynomial systems. The PBCS is implemented based on the state‐of‐the‐art Boolean Characteristic Set method (BCS). It adopts a master‐slave parallel pattern, and distributes tasks based on the polynomial sets after initial zero decomposition. We design a strategy of dynamically reallocating tasks to ameliorate load imbalance, which is caused by dynamical zero decomposition of polynomials. Furthermore, we improve its performance by optimizing the parameter settings of PBCS, including the maximum number of polynomial branches that trigger the dynamic allocation policy and the scheduling time. Experimental results with solving several Boolean polynomial systems confirm that PBCS is efficient and scalable, especially for the equations generating from stream ciphers that have block triangular structure. Moreover, the method also has good scalability. It shows a stable speedup as well even extending to the size of thousands of CPU cores.

Typical speech separation systems usually operate in the time-frequency (T-F) domain by enhancing the magnitude response and leaving the phase response unaltered. Recent studies, however, suggest that phase is important for perceptual quality, leading some researchers to consider magnitude and phase spectrum enhancements. The merging of the complex ideal ratio masking (cIRM) estimation and training with deep neural network (DNN) has been proved to be an effective way to improve speech separation. Furthermore, the label ambiguity (or permutation) problem has become a major barrier for speaker-independent multi-talker source separation, which prompts us to come up with new solutions. In this paper, to solve the problem of speaker-independent monaural source separation, we propose a novel method called pcIRM, which creatively achieves the cIRM estimation with the utterance-level permutation invariant training (uPIT). Specifically, pcIRM is implemented with the deep bidirectional LSTM (Bi-LSTM) RNN network, and evaluated with the WSJ0-2mix datasets. We report separation results for the proposed method and compare them to that of the existing state-of-the-art methods. Extensive experimental results demonstrate the advantages of our proposed pcIRM method in terms of the signal-to-distortion ratio (SDR) metric.

Summary Hundreds of millions of Chinese people have become social network users in recent years, and aligning the accounts of common Chinese users across multiple social networks is valuable to many inter‐network applications, for example, cross‐network recommendation and cross‐network link prediction. Many methods have explored the proper ways of utilizing account name information into aligning the common English users' accounts. However, how to properly utilize the account name information when aligning the Chinese user accounts remains to be detailedly studied. In this article, we first discuss the available naming behavioral models as well as the related features for different types of Chinese account name matchings. Second, we propose the framework of Multi‐View Cross‐Network User Alignment (MCUA) method, which uses a multi‐view framework to creatively integrate different models to deal with different types of Chinese account name matchings, and can consider all of the studied features when aligning the Chinese user accounts. Finally, we conduct experiments to prove that MCUA can outperform many existing methods on aligning Chinese user accounts between Sina Weibo and Twitter. Besides, we also study the best learning models and the top‐k valuable features of different types of name matchings for MCUA over our experimental datasets.

The top-k dominating (TKD) query on skyline groups returns k skyline groups that dominate the maximum number of points in a given data set. The TKD query combines the advantages of skyline groups and top-k dominating queries, thus has been frequently used in decision making, recommendation systems, and quantitative economics. Traditional skylines are inadequate to answer queries from both individual and groups of points. The group size could be too large to be processed in a reasonable time as a single operator (i.e., the skyline group operator). In this paper, we address the performance problem of grouping for TKD queries in skyline database. We formulate the problem of grouping, define the group operator in skyline, and propose several efficient algorithms to find top-k skyline groups. Thus, we provide a systematic study of TKD queries on skyline groups and validate our algorithms with extensive empirical results on synthetic and realworld data.

Close set is a hypothesis utilized by the majority of machine-learning-based (ML-based) recognition algorithms, assuming all testing classes are known at training time. In real world, the more practical model is Open Set Recognition (OSR), which allows the presence of unknown classes at testing time, but requires the rejection ability of the model. The compact abating probability (CAP) model, which assumes the probability of class membership decreases in value (abates) as points move from known data toward open space, is first raised in traditional ML-based OSR method and soon become the basis of majority of later developed works. Most of convolutional-neural-network-based (CNN-based) OSR methods also adopted this model as their basis. During our exploration, however, we find that the application of CAP model to the CNN-based OSR method is restricted by the difference of its feature space from that of ML-based method. To the best of our knowledge, we are the first group who find this gap. To fill this gap, we propose a method called OpenSoftMax to transform the CNN-based methods' features by the process of SoftMax. In order to investigate performance, we further implement quantitative comparison between our OpenSoftMax method and the well-known CNN-based method OpenMax on caltech256 datasets. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposals.

Summary Workload mapping in cloud environment refers to map multiple workloads provided by the cloud users/tenants to the substrate network provided by the cloud providers, which is a NP‐hard problem. The workload is a service demand made to the cloud, which is modeled as a logical network consists of virtual nodes and virtual links. Substrate network is a physical network consists of physical nodes that are inter‐connected via communication links. Devising heuristic methods has become the mainstream of workload mapping problem, which can obtain a feasible solution, but the quality of the solution is not guaranteed. Pointing to this issue, this paper takes the mapping cost of the workloads as the solving objective, and models the workload mapping as a constraint optimization problem. Based on the constraint optimization model, we devise two algorithms to solve the problem. These algorithms can not only find the feasible solution, but also ensure the solution is optimal. Lastly, we have demonstrated the optimality of the proposed algorithms through theoretical proof and evaluated the performance of them through simulation experiment.

Summary The skyline query over uncertain data streams, as an important aspect of big data analysis, plays a significant role in domains such as environment monitoring, decision‐making, and data mining. The skyline query over uncertain data streams with sliding window model always focuses on the most recent N streaming items, which cannot meet the query requirements of different window scales at the same time. To improve the query flexibility and efficiency, we propose an efficient parallel method for processing uncertain n‐of‐N skyline queries; that is, computing the skyline for the most recent n (∀n ≤ N) items in parallel. Specifically, we first propose a framework for parallelizing the query computation for uncertain n‐of‐N skylines. Furthermore, we put forward a sliding window partitioning strategy as well as a streaming items mapping strategy to realize the load balance for each node. In addition, we define a spatial index structure RST based on R‐tree to organize the elements within each individual sliding window and candidate set in each which can significantly improve the dominance tests. Most importantly, we provide an encoding interval scheme to transform the n‐of‐N query into stabbing query in each compute node, which can greatly minimize the query scope and improve the query efficiency. In addition, we use a red‐black tree named RBI to store all stabbing intervals. Extensive experimental results demonstrate that the proposals are efficient and can greatly meet the query requirement of users in real applications.

Summary The Atmospheric General Circulation Model (AGCM) as one of the most important components of Climate System Model (CSM), has been proved to be an effective way for weather forecasting and climate prediction. Although lots of efforts have been conducted to improve the computing efficiency of AGCMs, such as exploit parallel algorithms, migrating codes, and even redesigning systems to adapt to the emerging computer architectures, it is not enough to match the real requirement, due to the limited scalability of the parallel algorithms themselves. Therefore, we design and implement a scalable parallel spectral‐based atmospheric circulation mode called PAGCM in this paper. Specifically, we first analyze the data dependencies of the dimensions in different spaces according to the calculation characteristics of spectral models, and based on which we propose a two‐dimensional decomposition algorithm in PAGCM to effectively increase the involving cores for the parallel computing, and thus reduce the overall computing time. Furthermore, to adapt to the novel data decomposition in each computing stage of dynamic framework, we propose three‐dimensional data transposition algorithms and data collection algorithms correspondingly, by considering of load balancing and communication optimization. Extensive experiments are conducted on Tianhe‐2 to validate the effectiveness and scalability of our proposals.

Solving Boolean polynomial systems as an important aspect of symbolic computation, plays a fundamental role in various real applications. Although there exist many efficient sequential algorithms for solving Boolean polynomial systems, they are inefficient or even unavailable when the problem scale becomes large, due to the computational complexity of the problem and the limited processing capability of a single node. In this paper we propose an efficient parallel characteristic set method called PBCS for solving Boolean polynomial systems under the high-performance computing environment. Specifically, PBCS takes full advantage of the state-of-the-art characteristic set method and achieves load balancing by dynamically reallocating tasks. Moreover, the performance is further improved by optimizing the parameter setting. Extensive experiments are conducted to demonstrate that PBCS is efficient and scalable for solving Boolean equations, especially for the equations rasing from stream ciphers that have block triangular structure. In addition, the algorithm has good scalability and can be extended to the size of thousands CPU cores with a stable speedup.

Job scheduling in HPC systems by default allocate adjacent compute nodes for jobs for lower communication overhead. However, it is no longer applicable to data-intensive jobs running on systems with I/O forwarding layer, where each I/O node performs I/O on behalf of a subset of compute nodes in the vicinity. Under the default node allocation strategy a job's nodes are located close to each other and thus it only uses a limited number of I/O nodes. Since the I/O activities of jobs are bursty, at any moment only a minority of jobs in the system are busy processing I/O. Consequently, the bursty I/O traffic in the system is also concentrated in space, making the load on I/O nodes highly unbalanced. In this paper, we use the job logs and I/O traces collected from Tianhe-1A to quantitatively analyze the two causes of spatially bursty I/O, including uneven I/O traffic of job's processes and uneven distribution of job's nodes. Based on the analysis we propose a node allocation strategy that takes account of processes' different amounts of I/O traffic, so that the I/O traffic can be processed by more I/O nodes more evenly. Our evaluations on Tianhe-1A with synthetic benchmarks and realistic applications show that the proposed strategy can further exploit the potential of I/O forwarding layer and promote the I/O performance.

Summary I/O forwarding layer has now become a standard storage layer in today's HPC systems in order to scale current storage systems to new levels of concurrency. With the deepening of storage hierarchy, I/O requests must traverse through several types of nodes to access required data, including compute nodes, I/O nodes, and storage nodes. It becomes difficult to control the data path and apply cross‐layer I/O optimization. In this paper, we propose a well coordinated I/O stack, which coordinates the data path between compute nodes and I/O nodes for better load balancing and data locality with a job‐level I/O node mapping, and coordinates data path between I/O nodes and storage nodes for lighter I/O interference. We implement and evaluate our ideas on Tianhe‐1A by leveraging an open‐source I/O forwarding layer named IOFSL. The experimental results show that our proposals can significantly accelerate I/O performance of multiple I/O kernels and real applications.

Conjunctive Boolean query is one fundamental operation for document retrieval in many information systems and databases. Various algorithms have been put up in terms of maximizing the query efficiency. In recent years, researchers began to exploit the parallel advantage of single-instruction-multiple-data (SIMD) instructions to accelerate the intersection procedure and achieved substantial gains over previous scalar algorithms. However, these works only focus on intersecting two sets at a time and ignore the scenario of multiple sets intersection. We present a flexible search algorithm which balances non-SIMD and SIMD comparisons in order to provide efficient and effective intersection.

Summary The increasing volume of uncertain data has resulted in a dire need for supporting efficient uncertain data management. The skyline query as an important aspect of data management has received considerable attention in recent years, because of its importance in making intelligent decisions over complex data. Moreover, data collection and storage have become increasingly distributed, which makes the central assembly of data for storage and query infeasible and inefficient. Although many research efforts have been conducted to address the skyline query problem in various distributed scenarios, we still lack algorithms to address the queries over interval data, which is a special kind of attribute‐level uncertain data that widely exists in many applications. In this paper, we extensively study the skyline query over distributed interval data. We model the skyline query problem and define the distributed skyline query over interval data. Particularly, 2 efficient algorithms are proposed to retrieve the skylines progressively from distributed local sites with a highly optimized feedback framework. Moreover, we exploit 2 strategies for further improving the queries. Extensive experiments on synthetic and real datasets with real deployment are conducted to validate the effectiveness and efficiency of our proposals.

Summary Skyline computation is particularly useful in multi‐criteria decision‐making applications. However, it is inadequate to answer queries that need to analyze not only individual points but also groups of points. Compared to the traditional skyline computation, computing group‐based skyline is much more complicated and expensive. This computational challenge promotes us to use modern computing platforms to accelerate the computation. In this paper, we introduce a novel multi‐core algorithm to compute group‐based skyline. We first compute the skyline layers of a data set in parallel, which are a critical intermediate result. In the algorithm, we maintain an efficiently updatable data structure for the shared global skyline layers, which is used to minimize dominance tests and maintain high throughput. Then we design an efficient parallel algorithm to find group‐based skyline based on the skyline layers. Extensive experimental results on real and synthetic data sets show that our algorithms achieve 10‐fold speedup with 16 parallel threads over state‐of‐the‐art sequential algorithms on challenging workloads.

Summary Query processing over uncertain preferences is very common in real‐life situations, because many times, we cannot model users' preferences as strict partial orders. In this paper, we investigate skyline queries over uncertain preferences. The latest state‐of‐the‐art algorithm, calledUsky‐basealgorithm, makes significant advances. However, it still needs to be perfected in 2 aspects. (1) Theoretic analysis: The correctness of the algorithm is not fully verified. (2) Efficiency: Due to the heavy calculation introduced by adoptinginclusion‐exclusion principleto express the skyline probability, it needs massive time when computing skyline probabilities for large data sets. To address the above 2 concerns, we first review theUsky‐basealgorithm and lemmas it based on. Then we propose a novel parallel algorithm, calledParallel‐sky, to compute skyline probability of a given object. Moreover, we propose an adding algorithm and a deleting algorithm to deal with dynamic scenarios where new objects are added in and outdated objects are deleted out. Furthermore, we extend our algorithm from computing skyline probability of a given object to all objects in a data set. We conduct extensive experiments on real and synthetic data sets to validate the effectiveness and efficiency of our proposals.

Summary The problem of flash data dissemination refers to transmitting time‐critical data to a large group of distributed receivers in a timely manner, which widely exists in many mission‐critical applications and Web services. However, existing approaches for flash data dissemination fail to ensure the timely and efficient transmission, because of the unpredictability of the dissemination process. Overlay routing has been widely used as an efficient routing primitive for providing better end‐to‐end routing quality by detouring inefficient routing paths in the real networks. To improve the predictability of the flash data dissemination process, we propose a bandwidth and latency sensitive overlay routing approach named BLOR, by optimizing the overlay routing and avoiding inefficient paths in flash data dissemination. BLOR tries to select optimal routing paths in terms of network latency, bandwidth capacity, and available bandwidth in nature, which has never been studied before. Additionally, a location‐aware unstructured overlay topology construction algorithm, an unbiased top‐kdominance model, and an efficient semi‐distributed information management strategy are proposed to assist the routing optimization of BLOR. Extensive experiments have been conducted to verify the effectiveness and efficiency of the proposals with real‐world data sets. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY Characterized by the emergence of a large number of live content, the emergency applications have received increasing attention in recent years. Providing a general and scalable event, matching service can precisely notify users latest information that they are interested in. However, because the live content arrival rate may churn significantly in a short time and subscriptions with various patterns tend to be skewed, it is challenging to increase the generality, scalability, and elasticity of the matching process. We propose a novel parallel event matching service based on the cloud computing environment, called GSEM, to satisfy these requirements. GSEM first presents a two‐hop framework and a general subscription pattern to handle various patterns of subscriptions. To provide scalable matching service, ahybrid content space partitionscheme is proposed to divide large skewed subscriptions into multiple small clusters managed by a group of parallel servers. To adapt to the sudden change of event arrival rate, GSEM elastically adjusts the scale of servers and rebalances their workloads through aperformance‐aware detectiontechnique. A prototype deployment on the OpenStack platform shows that GSEM achieves scalable matching throughput with the growth of servers, elastic service capacity with the change of event arrival rate, and significantly outperforms the existing cloud based systems in various workloads. Copyright © 2014 John Wiley & Sons, Ltd.

The skyline query processing over uncertain data streams has received considerable attention, due to its importance in helping users make intelligent decisions over complex data. Nevertheless, existing studies only focus on retrieving the skylines over data streams in a centralized environment typically with one processor, which limits the scalability of algorithms and cannot meet the requirement for massive data analysis. The emerging cloud computing environment provides much more reliable and stable environments than the traditional distributed environments, which can be well adapted to the massive data management and complex queries. Unfortunately, existing parallel frameworks in clouds such as MapReduce and its variants are not suitable for the skyline queries over uncertain data streams. In this paper, we propose a general framework for parallelizing the probabilistic streaming skyline operator with the sliding window partitioning. Particularly, we propose four items mapping strategies CMS, AMS, DMS and APS to optimize the queries based on the proposed parallel framework. Extensive experiments with real deployment are conducted to demonstrate the effectiveness and efficiency of the proposals.

Network virtualization is recognized as an effective way to overcome the ossification of the Internet. However, the virtual network mapping problem (VNMP) is a critical challenge, focusing on how to map the virtual networks to the substrate network with efficient utilization of infrastructure resources. The problem can be divided into two phases: node mapping phase and link mapping phase. In the node mapping phase, the existing algorithms usually map those virtual nodes with a complete greedy strategy, without considering the topology among these virtual nodes, resulting in too long substrate paths (with multiple hops). Addressing this problem, we propose a topology awareness mapping algorithm, which considers the topology among these virtual nodes. In the link mapping phase, the new algorithm adopts the k -shortest path algorithm. Simulation results show that the new algorithm greatly increases the long-term average revenue, the acceptance ratio, and the long-term revenue-to-cost ratio ( R/C ).