Account migration in sharded blockchains presents a critical trade-off between optimization effectiveness and system availability. While dynamically reallocating accounts across shards can significantly reduce cross-shard transaction overhead, existing migration mechanisms cause service disruptions that intensify as state data volumes grow. To address this challenge, we propose BIND, a batch-wise account migration protocol that eliminates service interruptions by enabling continuous transaction processing throughout migration. BIND introduces a dual transaction pool architecture that isolates transactions involving migrating accounts while allowing non-migrating accounts to operate uninterrupted. To optimize migration efficiency, we design a reverse greedy heuristic algorithm that partitions accounts into batches based on community cohesion, maximizing intra-batch connectivity to front-load cross-shard communication reduction. We evaluate BIND using real Ethereum transactions, demonstrating superior performance over existing mechanisms. BIND achieves 12% higher overall throughput, reduces migration time to 23.6%-39.3% of the one-shot baseline (across 1-10Gbps bandwidth), and lowers cross-shard transaction rates by 24.1% compared to random batching. These results confirm BIND as a practical solution for large-scale, non-disruptive account migration in production sharded blockchains.

Dynamic graph anomaly detection (DGAD) is essential for iden- tifying anomalies in evolving graphs across domains such as fi- nance and social networks. Recently, generalist graph anomaly detection (GAD) models have shown promising results. They are pretrained on multiple source datasets and generalize across do- mains. While effective on static graphs, they struggle to capture evolving anomalies in dynamic graphs. Moreover, the continuous emergence of new domains and the lack of labeled data further challenge generalist DGAD. Effective cross-domain DGAD requires both domain-specific and domain-agnostic anomalous patterns. Importantly, these patterns evolve temporally within and across domains. Building on these insights, we propose a DGAD model with Dynamic Prototypes (DP) to capture evolving domain-specific and domain-agnostic patterns. Firstly, DP-DGAD extracts dynamic prototypes, i.e., evolving representations of normal and anomalous patterns, from temporal ego-graphs and stores them in a memory buffer. The buffer is selectively updated to retain general, domain- agnostic patterns while incorporating new domain-specific ones. Then, an anomaly scorer compares incoming data with dynamic prototypes to flag both general and domain-specific anomalies. Fi- nally, DP-DGAD employs confidence detection guided memory buffer updating for effective adaptation to target domain. Extensive experiments demonstrate state-of-the-art performance across ten real-world datasets from different domains.

As deep learning (DL) continues to advance, effective feature extraction from large-scale data remains crucial for enhancing model performance. To leverage the advantages of the frequency domain, such as concentrated signal energy, prominent data features, and rich detailed characteristics, this paper proposes a novel frequency-domain feature extraction method. However, existing frequency component selection algorithms often struggle to adapt to diverse tasks, tend to yield only locally optimal solutions, and require prolonged processing times. To overcome these limitations, we introduce the Adaptive Fast Frequency Selection (AFFS) algorithm, which seamlessly integrates a frequency component selection factor layer into DL models to identify globally optimal frequency combinations suited to various downstream tasks. We further analyze the relationship between selected frequency components and model performance, providing theoretical guarantees regarding optimality, robustness, and generalization error bounds. Moreover, a fast selection procedure is developed to exploit the empirically observed rapid convergence of the selection-factor ranking, significantly accelerating the selection process. Extensive experiments on five datasets, ten DL models, and two subsequent tasks demonstrate that AFFS achieves superior performance: even when the input data size is reduced to only 10% of the original frequency features, model classification accuracy improves by approximately 1%, while the early stopping mechanism shortens the selection process by about 80%.

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Sea surface temperature (SST) prediction is a critical task in ocean science, supporting various applications, such as weather forecasting, fisheries management, and storm tracking. While existing data-driven methods have demonstrated significant success, they often neglect to leverage the rich domain knowledge accumulated over the past decades, limiting further advancements in prediction accuracy. The recent emergence of large language models (LLMs) has highlighted the potential of integrating domain knowledge for downstream tasks. However, the application of LLMs to SST prediction remains under explored, primarily due to the challenge of integrating ocean domain knowledge and numerical data. To address this issue, we propose Ocean Knowledge Graph-enhanced LLM (OKG-LLM), a novel framework for global SST prediction. To the best of our knowledge, this work presents the first systematic effort to construct an Ocean Knowledge Graph (OKG) specifically designed to represent diverse ocean knowledge for SST prediction. We then develop a graph embedding network to learn the comprehensive semantic and structural knowledge within the OKG, capturing both the unique characteristics of individual sea regions and the complex correlations between them. Finally, we align and fuse the learned knowledge with fine-grained numerical SST data and leverage a pre-trained LLM to model SST patterns for accurate prediction. Extensive experiments on the real-world dataset demonstrate that OKG-LLM consistently outperforms state-of-the-art methods, showcasing its effectiveness, robustness, and potential to advance SST prediction. The codes are available in the online repository.

Blockchain interoperability protocols enable cross-chain asset transfers or data retrievals between isolated chains, which are considered as one of the core infrastructure for Web 3.0. However, existing protocols either face severe scalability issues due to high on-chain and off-chain cost, or suffer from trust concerns because of centralized architecture.

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data presents some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this article, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.

Accurate spatial-temporal (ST) traffic prediction plays an essential role in intelligent transportation systems. Existing advanced traffic prediction methods typically utilize spatial-temporal graph neural networks (STGNNs) to capture the ST correlations and achieve excellent prediction performance. However, our experimental investigation reveals that existing static and dynamic graph-based STGNNs still incur excessive noise and redundancy, and fail to discover robust and reliable ST correlations in traffic networks. Moreover, most methods cannot explain the underlying reasons behind the ST correlations. To solve these problems, we propose a novel S patial- T emporal G raph M odeling framework via A daptive contrastive learning (ST-GMA). Firstly, we design a robust augmentation learning module to generate high-level and robust data augmentations via a self-supervised task for modeling reliable correlations. Then, we develop an adaptive contrastive learning module to update correlation graphs by effectively selecting positive and negative augmentations, reducing redundant calculations, and providing insights into the correlation changes. Finally, ST-GMA integrates the generated correlation graphs with ST convolution blocks to conduct traffic prediction tasks. Experimental results on five real-world datasets demonstrate that ST-GMA not only achieves significant prediction performance compared with state-of-the-art methods but also exhibits a new perspective on the interpretability of correlation changes.

Distribution shifts from external events and new entities can significantly compromise spatial-temporal prediction accuracy, potentially leading to severe outcomes like traffic accidents. Existing methods often fail under these conditions due to two main limitations: they focus on invariant patterns, missing the diversity required to capture the evolving dynamics of distribution shifts; they rely on often inaccessible future knowledge, such as spatial information of new entities, limiting their generalizability. To address these limitations, we formally define the problem of inductive spatial-temporal prediction under continuous distribution shifts and introduce the Contrastive Learning Based Inductive Graph Neural Network (COIN-GNN) as a solution. We develop a novel metric, Relation Importance (RI), to effectively select stable entities and distinct spatial relationships, forming an informative subgraph. Additionally, we construct an informative temporal memory buffer to store and review influential timestamps identified using influence functions. COIN-GNN then generates pseudo-observations for unstable and uninformative entities during these influential timestamps, simulating potential distribution shifts. By applying contrastive learning, the network learns stable and informative representations that can effectively counter distribution shifts without relying on future knowledge. Our extensive experiments on several real-world datasets—from traffic to weather—demonstrate COIN-GNN’s superior performance across different domains without requiring future knowledge.

Abstract Graph neural networks (GNNs) have emerged due to their success at modeling graph data. Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs come into play. To avoid communication caused by expensive data movement between workers, we propose Sancus and its advanced version Sancus"Equation missing", the staleness and quantization-aware communication-avoiding decentralized GNN system. By introducing a set of novel bounded embedding staleness metrics and adaptively skipping broadcasts, Sancus abstracts decentralized GNN processing as sequential matrix multiplication and uses historical embeddings via cache. To further mitigate the communication volume, Sancus"Equation missing" conducts quantization-aware communication on embeddings to reduce the size of broadcast messages. Theoretically, we show bounded approximation errors of embeddings and gradients with a known fastest convergence guarantee. Empirically, we evaluate Sancus and Sancus"Equation missing" with common GNN models via different system setups on large-scale benchmark datasets. Compared to SOTA works, Sancus"Equation missing" can avoid up to $$86\%$$ 86 % communication with $$3.0\times $$ 3.0 × faster throughput on average without accuracy loss.

Cryptocurrencies are rapidly expanding and becoming vital in digital financial markets. However, the rise in cryptocurrency-related illicit activities has led to significant losses for users. To protect the security of these platforms, it is critical to identify illicit accounts effectively. Current detection methods mainly depend on feature engineering or are inadequate to leverage the complex information within cryptocurrency transaction networks, resulting in suboptimal performance. In this paper, we present DIAM, an effective method for detecting illicit accounts in cryptocurrency transaction networks modeled by directed multi-graphs with attributed edges. DIAM first features an Edge2Seq module that captures intrinsic transaction patterns from parallel edges by considering edge attributes and their directed sequences, to generate effective node representations. Then in DIAM, we design a multigraph Discrepancy (MGD) module with a tailored message passing mechanism to capture the discrepant features between normal and illicit nodes over the multigraph topology, assisted by an attention mechanism. DIAM integrates these techniques for end-to-end training to detect illicit accounts from legitimate ones. Extensive experiments, comparing against 15 existing solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum, demonstrate that DIAM consistently outperforms others in accurately identifying illicit accounts. For example, on a Bitcoin dataset with 20 million nodes and 203 million edges, DIAM attains an F1 score of 96.55%, markedly surpassing the runner-up's score of 83.92%. The code is available at https://github.com/TommyDzh/DIAM.

Graph-level representation learning is important in a wide range of applications. Existing graph-level models are generally built on i.i.d. assumption for both training and testing graphs. However, in an open world, models can encounter out-of-distribution (OOD) testing graphs that are from different distributions unknown during training. A trustworthy model should be able to detect OOD graphs to avoid unreliable predictions, while producing accurate in-distribution (ID) predictions. To achieve this, we present SGOOD, a novel graph-level OOD detection framework. We find that substructure differences commonly exist between ID and OOD graphs, and design SGOOD with a series of techniques to encode task-agnostic substructures for effective OOD detection. Specifically, we build a super graph of substructures for every graph, and develop a two-level graph encoding pipeline that works on both original graphs and super graphs to obtain substructure-enhanced graph representations. We then devise substructure-preserving graph augmentation techniques to further capture more substructure semantics of ID graphs. Extensive experiments against 11 competitors on numerous graph datasets demonstrate the superiority of SGOOD, often surpassing existing methods by a significant margin. The code is available at https://github.com/TommyDzh/SGOOD.

Recent studies successfully learned static graph embeddings that are structurally fair by preventing the effectiveness disparity of high- and low-degree vertex groups in downstream graph mining tasks. However, achieving structure fairness in dynamic graph embedding remains an open problem. Neglecting degree changes in dynamic graphs will significantly impair embedding effectiveness without notably improving structure fairness. This is because the embedding performance of high-degree and low-to-high-degree vertices will significantly drop close to the generally poorer embedding performance of most slightly changed vertices in the long-tail part of the power-law distribution. We first identify biased structural evolutions in a dynamic graph based on the evolving trend of vertex degree and then propose FairDGE, the first structurally Fair Dynamic Graph Embedding algorithm. FairDGE learns biased structural evolutions by jointly embedding the connection changes among vertices and the long-short-term evolutionary trend of vertex degrees. Furthermore, a novel dual debiasing approach is devised to encode fair embeddings contrastively, customizing debiasing strategies for different biased structural evolutions. This innovative debiasing strategy breaks the effectiveness bottleneck of embeddings without notable fairness loss. Extensive experiments demonstrate that FairDGE achieves simultaneous improvement in the effectiveness and fairness of embeddings.

Robust PCA is a popular anomaly detection technique and has been widely used in many applications. Although Robust PCA is promising, it is usually designed in a two-order matrix form, which is inferior to the tensor that can capture multilinearity features of data. Moreover, the detection accuracy under Robust PCA further suffers due to its sensitivity to the rank parameter which is hard to set in practice and the limitation of PCA method in capturing the non-linear feature in the data. To address the issues, we propose a Robust Tensor Convolutional Autoencoder (RTCAE) where the autoencoder instead of SVD is exploited to recover the normal data from the corrupted measurement tensor data. However, directly exploiting deep autoencoder may suffer from the problem of high memory consumption and computation overhead due to the large number of parameters used in autoencoder. To make our anomaly detection lightweight, we further design a Light Convolutional Autoencoder (LightCAE) which contains a compressed autoencoder by exploiting tensor factorization to largely compress the parameters while significantly reducing the computation complexity. We conduct extensive experiments on three real data traces to compare the performance of our proposed schemes (RTCAE and lightCAE) with that of seven baseline algorithms. The experiment results demonstrate that our proposed RTCAE achieves the highest anomaly detection accuracy. Moreover, our LightCAE requires over 60 times smaller memory storage than that required in RTCAE while achieving the similar anomaly detection accuracy.

Abstractive summarization aims to generate a concise summary covering the input document's salient information. Within a report document, the salient information can be scattered in the textual and non-textual content. However, existing document summarization datasets and methods usually focus on the text and filter out the non-textual content. Missing tabular data can limit produced summaries' informativeness, especially when summaries require covering quantitative descriptions of critical metrics in tables. Existing datasets and methods cannot meet the requirements of summarizing long text and dozens of tables in each report document. To deal with the scarcity of available datasets, we propose FINDSum, the first large-scale dataset for long text and multi-table summarization. Built on 21,125 annual reports from 3,794 companies, FINDSum has two subsets for summarizing each company's results of operations and liquidity. Besides, we present four types of summarization methods to jointly consider text and table content when summarizing reports. Additionally, we propose a set of evaluation metrics to assess the usage of numerical information in produced summaries. Our summarization methods significantly outperform advanced baselines, which verifies the necessity of incorporating textual and tabular data when summarizing report documents. We also conduct extensive comparative experiments to identify vital model components and configurations that can improve summarization results.

POI recommendation is practically important to facilitate various Location-Based Social Network (LBSN) services, and has attracted rising research attention recently. Existing works generally assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. However, in real application scenarios, the check-in data can be rather unreliable (e.g. sparse, incomplete and inaccurate) due to both subjective and objective causes including positioning error and user privacy concerns. The data uncertainty issue may lead to significant negative impacts on the performance of the POI recommendation, but is not fully explored by existing works. To this end, this paper investigates a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network (BayMAN for short) to effectively address it. Specifically, we construct three POI graphs to comprehensively model the dependencies among the POIs from different views, including the personal POI transition graph, the semantic-based POI graph and distance-based POI graph. As the personal POI transition graph is usually sparse and sensitive to noise, we design a Bayes-enhanced spatial dependency learning module for data augmentation from the local view. A Bayesian posterior guided graph augmentation approach is adopted to generate a new graph with collaborative signals to increase the data diversity. Then both the original and the augmented graphs are used for POI representation learning to counteract the data uncertainty issue. Next, the POI representations of the three view graphs are input into the proposed multi-view attention-based user preference learning module. By incorporating the semantic and distance correlations of POIs, the user preference can be effectively refined and finally robust recommendation results are achieved. We conduct extensive experiments over three real-world LSBN datasets. The results show that BayMAN significantly outperforms the state-of-the-art methods in POI recommendation when the available check-ins are incomplete and noisy.

Generating appropriate emotions for responses is essential for dialogue systems to provide human-like interaction in various application scenarios. Most previous dialogue systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the emotional expressions of humans are rooted in personality traits. Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialogue system and further investigate a solution through the personality-affected mood transition. Specifically, we first construct a daily dialogue dataset, Personality EmotionLines Dataset ( PELD ), with emotion and personality annotations. Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialogue context. Finally, we propose to model the personality as the transition weight by simulating the mood transition process in the dialogue system and solve the challenges above. We conduct extensive experiments on PELD for evaluation. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.

Personalized federated learning (PFL) has emerged as a paradigm to provide a personalized model that can fit the local data distribution of each client. One natural choice for PFL is to leverage the fast adaptation capability of meta-learning, where it first obtains a single global model, and each client achieves a personalized model by fine-tuning the global one with its local data. However, existing meta-learning-based approaches implicitly assume that the data distribution among different clients is similar, which may not be applicable due to the property of data heterogeneity in federated learning. In this work, we propose a Group-based Federated Meta-Learning framework, called G-FML , which adaptively divides the clients into groups based on the similarity of their data distribution, and the personalized models are obtained with meta-learning within each group. In particular, we develop a simple yet effective grouping mechanism to adaptively partition the clients into multiple groups. Our mechanism ensures that each group is formed by the clients with similar data distribution such that the group-wise meta-model can achieve “personalization” at large. By doing so, our framework can be generalized to a highly heterogeneous environment. We evaluate the effectiveness of our proposed G-FML framework on three heterogeneous benchmarking datasets. The experimental results show that our framework improves the model accuracy by up to 13.15% relative to the state-of-the-art federated meta-learning.

Crowd flow prediction, which aims to predict the in/out flows of different areas of a city, plays an important role in various applications like intelligent transportation. The challenges of this problem lie in both dynamic mobility patterns of crowds and complex spatial-temporal correlations. Meanwhile, crowd flow is highly correlated to and affected by the Origin-Destination (OD) locations of the flow trajectories, which is largely ignored by existing works. In this paper, we study the novel problem of predicting the crowd flow and flow OD simultaneously, and propose a multi-task bayes-enhanced adversarial spatial temporal network entitled MBA-STNet. MBA-STNet adopts a shared-private framework that contains private spatial-temporal encoders, a shared spatial-temporal encoder, and decoders to learn the task-specific features and shared features. To effectively extract discriminative shared features, an adversarial loss on shared feature extraction is incorporated to reduce information redundancy. A Bayesian Heterogeneous Spatio-temporal Attention Network is designed to learn complex spatio-temporal correlations and alleviate data uncertainty. We also design an attentive temporal queue to capture the complex temporal dependency automatically without domain knowledge. Extensive evaluations are conducted over the bike and taxicab trip datasets in New York. The results demonstrate that the proposed MBA-STNet is superior to state-of-the-art methods.

Abnormal traffic incidents such as traffic accidents have become a significant health and development threat with the rapid urbanization of many countries. Thus it is critically important to accurately forecast the traffic accident risks of different areas in a city, which has attracted increasing research interest in the research area of urban computing. The challenges of accurate traffic risk forecasting are three-fold. First, traffic accident data in some areas of a city is sparse, especially for a fine-grained prediction, which may cause the zero inflation problem during model training. Second, the spatio-temporal correlations of the traffic accidents occurring in different areas are rather complex and non-linear, which is difficult to capture by existing shallow models like regression. Third, the occurrence of traffic accidents can be significantly affected by various context features including weather, POI and road network features. It is non-trivial to capture the complex associations between the diverse context features and traffic accident risks for building an accurate prediction model. To address the above challenges, this paper proposes a Multi-View Multi-Task Spatio-Temporal Networks (MVMT-STN) model to forecast fine- and coarse-grained traffic accident risks of a city simultaneously. Specifically, to address the data sparsity issue in a fine-grained prediction, we adopt a multi-task learning framework to jointly forecast both fine- and coarse-grained traffic accident risks by considering their spatial associations. For each granularity prediction, we design the channel-wise CNN and multi-view GCN to capture the local geographic dependency and global semantic dependency, respectively. In order to obtain the diverse impacts of the context features on traffic accidents, we also introduce a fusion learning module that integrates the channel-wise and multi-view features learned from different types of the external factors. We conduct extensive experiments over two large real traffic accident datasets. The results show that MVMT-STN improves the performance of traffic accident risk prediction in both fine- and coarse-grained prediction by a large margin compared with existing state-of-the-art methods.

Dynamic graph embedding learns representation vectors for vertices and edges in a graph that evolves over time. We aim to capture and embed the evolution of vertices' temporal connectivity. Existing work studies the vertices' dynamic connection changes but neglects the time it takes for edges to evolve, failing to embed temporal linkage information into the evolution of the graph. To capture vertices' temporal linkage evolution, we model dynamic graphs as a sequence of snapshot graphs, appending the respective timespans of edges (ToE). We co-train a linear regressor to embed ToE while inferring a common latent space for all snapshot graphs by a matrix-factorization-based model to embed vertices' dynamic connection changes. Vertices' temporal linkage evolution is captured as their moving trajectories within the common latent representation space. Our embedding algorithm converges quickly with our proposed training methods, which is very time efficient and scalable. Extensive evaluations on several datasets show that our model can achieve significant performance improvements, i.e. 22.98% on average across all datasets, over the state-of-the-art baselines in the tasks of vertex classification, static and time-aware link prediction, and ToE prediction.

Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of such dynamics where the evolution of each local structure starts at different times and lasts for various durations. To maintain asynchronous structural evolutions within the graph, we innovatively formulate dynamic graphs as temporal edge sequences associated with joining time of vertices (ToV) and timespan of edges (ToE). Then, a time-aware Transformer is proposed to embed vertices’ dynamic connections and ToEs into the learned vertex representations. Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information. Extensive evaluations on several datasets show that our approach outperforms the state-of-the-art in a wide range of graph mining tasks. At the same time, it is very efficient and scalable for embedding large-scale dynamic graphs.

Computational color naming (CCN) aims to learn a mapping from pixels into semantic color names, e.g., red, green and blue. CCN has wide applications including color vision deficiency assistance and color image retrieval. Existing research on CCN mainly studies pixels collected under laboratory settings or studies images collected from the web. However, laboratory pixels are very limited such that the learned mapping may not generalize well on unseen pixels, and the mapping discovered from images is usually data-specific. In this paper, we aim to learn a universal mapping by studying pixels collected from the web. To this end, we formulate a novel classification problem that incorporates both the pixels and the RGB color model. The RGB color model is beneficial for learning the mapping because it characterizes the production of colors, e.g., the addition of red and green produces yellow. However, the characterization is rather qualitative. To solve this problem, we propose ColorMLP, which is a multilayer perceptron (MLP) embedded with graph attention networks (GATs). Here, the GATs are designed to capture color relations that we construct by referring to the RGB color model. In this way, the parameters of the MLP can be regularized to comply with the RGB model. We conduct comprehensive experiments to demonstrate the superiority of ColorMLP to alternative methods.To expand the application of CCN, we design a novel data augmentation method named partial color jitter (PCJ), which performs color jitter (CJ) on a subset of pixels belonging to the same color of an image. In this way, PCJ partially changes the color properties of images, thereby significantly increasing images’ diversity. We conduct extensive experiments on CIFAR10/100 and ImageNet datasets, showing that PCJ can consistently improve the classification performance. Our data and software can be found at https://https://github.com/yanzipei/CCN_and_ItsApp.

Graph neural networks (GNNs) have achieved great success on various graph tasks. However, recent studies have re-vealed that GNNs are vulnerable to adversarial attacks, including topology modifications and feature perturbations. Regardless of the fruitful progress, existing attackers require node labels and GNN parameters to optimize a bi-level problem, or cannot cover both topology modifications and feature perturbations, which are not practical, efficient, or effective. In this paper, we propose a black-box attacker PEEGA, which is restricted to access node features and graph topology for practicability. Specifically, we propose to measure the negative impact of various adversarial attacks from the perspective of node representations, thereby we formulate a single-level problem that can be efficiently solved. Furthermore, we observe that existing attackers tend to blur the context of nodes through adding edges between nodes with different labels. As a result, GNNs are unable to recognize nodes. Based on this observation, we propose a GNN defender GNAT, which incorporates three augmented graphs, i.e., a topology graph, a feature graph, and an ego graph, to make the context of nodes more distinguishable. Extensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of our proposed attacker, despite the fact that we do not access node labels and GNN parameters. Moreover, the effectiveness and efficiency of our proposed defender are also validated by substantial experiments.

Graph neural networks (GNNs) have emerged due to their success at modeling graph data. Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs come into play. To avoid communication caused by expensive data movement between workers, we propose Sancus, a staleness-aware communication-avoiding decentralized GNN system. By introducing a set of novel bounded embedding staleness metrics and adaptively skipping broadcasts, Sancus abstracts decentralized GNN processing as sequential matrix multiplication and uses historical embeddings via cache. Theoretically, we show bounded approximation errors of embeddings and gradients with convergence guarantee. Empirically, we evaluate Sancus with common GNN models via different system setups on large-scale benchmark datasets. Compared to SOTA works, Sancus can avoid up to 74% communication with at least 1.86X faster throughput on average without accuracy loss.

Spatio-temporal (ST) data is a collection of multiple time series data with different spatial locations and is inherently stochastic and unpredictable. An accurate prediction over such data is an important building block for several urban applications, such as taxi demand prediction, traffic flow prediction, and so on. Existing deep learning based approaches assume that outcome is deterministic and there is only one plausible future; therefore, cannot capture the multimodal nature of future contents and dynamics. In addition, existing approaches learn spatial and temporal data separately as they assume weak correlation between them. To handle these issues, in this article, we propose a stochastic spatio-temporal generative model (named D-GAN) which adopts Generative Adversarial Networks (GANs)-based structure for more accurate ST prediction in multiple time steps. D-GAN consists of two components: (1) spatio-temporal correlation network which models spatio-temporal joint distribution of pixels and supports a stochastic sampling of latent variables for multiple plausible futures; (2) a stochastic adversarial network to jointly learn generation and variational inference of data through implicit distribution modeling. D-GAN also supports fusion of external factors through explicit objective to improve the model learning. Extensive experiments performed on two real-world datasets show that D-GAN achieves significant improvements and outperforms baseline models.

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real-world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal data increase rapidly, traditional data mining methods, especially statistics-based methods for dealing with such data are becoming overwhelmed. Recently deep learning models such as recurrent neural network (RNN) and convolutional neural network (CNN) have achieved remarkable success in many domains due to the powerful ability in automatic feature representation learning, and are also widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, anomaly detection and classification. In this paper, we provide a comprehensive review of recent progress in applying deep learning techniques for STDM. We first categorize the spatio-temporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM. Next, we classify existing literature based on the types of spatio-temporal data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, on-demand service, climate & weather analysis, human mobility, location-based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions.

User profiling refers to inferring people’s attributes of interest ( AoIs ) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication. Nonlinguistic audio is coarse-grained audio data without linguistic content. It is collected due to privacy concerns in private situations like doctor-patient dialogues. The opportunity facilitates optimized organizational management and personalized healthcare, especially for chronic diseases. In this article, we are the first to build a user profiling system to infer gender and personality based on nonlinguistic audio. Instead of linguistic or acoustic features that are unable to extract, we focus on conversational features that could reflect AoIs. We firstly develop an adaptive voice activity detection algorithm that could address individual differences in voice and false-positive voice activities caused by people nearby. Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. According to the experimental evaluation of 100 people in 273 meetings, we achieved 0.759 and 0.652 in F1-score for gender identification and personality recognition, respectively.

User profiling refers to inferring people's attributes of interest (AoIs) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication. In this poster, we are the first to build a user profiling system to infer gender and personality based on nonlinguistic audio. Instead of linguistic or acoustic features which are unable to extract, we focus on conversational features that could reflect AoIs. We firstly develop an adaptive voice activity detection algorithm that could address individual differences in voice and false-positive voice activities caused by people nearby. Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. The experimental evaluation of 100 people in 273 meetings indicates the superiority of the proposed method in gender identification and personality recognition respectively.

Autonomous on-demand services, such as GOGOX (formerly GoGoVan) in Hong Kong, provide a platform for users to request services and for suppliers to meet such demands. In such a platform, the suppliers have autonomy to accept or reject the demands to be dispatched to him/her, so it is challenging to make an online matching between demands and suppliers. Existing methods use round-based approaches to dispatch demands. In these works, the dispatching decision is based on the predicted response patterns of suppliers to demands in the current round, but they all fail to consider the impact of future demands and suppliers on the current dispatching decision. This could lead to taking a suboptimal dispatching decision from the future perspective. To solve this problem, we propose a novel demand dispatching model using deep reinforcement learning. In this model, we make each demand as an agent. The action of each agent, i.e., the dispatching decision of each demand, is determined by a centralized algorithm in a coordinated way. The model works in the following two steps. (1) It learns the demand’s expected value in each spatiotemporal state using historical transition data. (2) Based on the learned values, it conducts a Many-To-Many dispatching using a combinatorial optimization algorithm by considering both immediate rewards and expected values of demands in the next round. In order to get a higher total reward, the demands with a high expected value (short response time) in the future may be delayed to the next round. On the contrary, the demands with a low expected value (long response time) in the future would be dispatched immediately. Through extensive experiments using real-world datasets, we show that the proposed model outperforms the existing models in terms of Cancellation Rate and Average Response Time.

Heterogeneous information network (HIN) embedding is to encode network structure into node representations with the heterogeneous semantics of different node and edge types considered. However, since each HIN may have a unique nature, e.g., a unique set of node and edge types, a model designed for one type of networks may not be applicable to or effective on another type. In this article, we thus attempt to propose a framework for HINs with arbitrary number of node and edge types. The proposed framework constructs a novel mixture-split representation of an HIN, and hence is named as MixSp. The mixture sub-representation and the split sub-representation serve as two different views of the network. Compared with existing models which only learn from the original view, MixSp thus may exploit more comprehensive information. Node representations in each view are learned by embedding the respective network structure. Moreover, the node representations are further refined through cross-view co-regularization. The framework is instantiated in three models which differ from each other in the co-regularization. Extensive experiments on three real-world datasets show MixSp outperforms several recent models in both node classification and link prediction tasks even though MixSp is not designed for a particular type of HINs.

Balanced rule-constrained resource allocation aims to evenly distribute tasks to different processors under allocation rule constraints. Conventional heuristic approach fails to achieve optimal solution while simple brute force method has the defect of high computational complexity. To address these limitations, we propose recursive balanced k-subset sum partition (RBkSP), in which iterative 'cut-one-out' policy is employed that in each round, only one subset whose weight of tasks sums up to 1/k of the total weight of all tasks is taken out from the set. In a single partition, we first create a dynamic programming table with its elements recursively computed, then use 'zig-zag search' method to explore the table, find out elements with optimal subset partition and assign different partitions to proper places. Next, to resolve conflicts during allocation, we use simple but effective heuristic method to adjust the allocation of tasks that is contradicted to allocation rules. Testing results show RBkSP can achieve more balanced results with lower computational complexity over classical benchmarks.

Stationless bike-sharing systems such as Mobike are currently becoming extremely popular in China as well as some other big cities in the world. Compared to traditional bicycle-sharing systems, stationless bike-sharing systems do not need bike stations. Users can rent and return bikes at arbitrary locations through an App installed on their smart phones. Such a convenient and flexible bike-sharing mode greatly solves the last mile issue of the commuters, and better meets their real bike usage demand. However, it also poses new challenges for operators to manage the system. The first primary challenge is how to accurately estimate the real bike usage demand in different areas of a city and in different time intervals, which is crucial for the system planning and operation. This paper for the first time proposes a data driven approach for bike usage demand inference in stationless bike-sharing systems. The idea is that we first estimate the demands in some regions and time intervals from a small number of observed bike check-out/in data directly, and then use them as seeds to infer the region-level bike usage demands of an entire city. Specifically, we formulate this problem as a matrix completion task by modeling the bike usage demand as a matrix whose two dimensions are time intervals of a day and regions of a city, respectively. With the observation that POI distribution of a region is an important indicator to bike demand, we propose to utilize inductive matrix factorization by considering POIs as side information. As the bike usage data are highly correlated in both spatial and temporal dimensions, we also incorporate the spatial-temporal correlations as well as the balanced bike usage constraint into a joint optimization framework. We evaluate the proposed model on a large Mobike trip dataset collected from Beijing, and the experimental results show its superior performance by comparison with various baseline methods.

Network embedding has been widely employed in networked data mining applications as it can learn low-dimensional and dense node representations from the high-dimensional and sparse network structure. While most existing network embedding methods only model the proximity between two nodes regardless of the order of the proximity, this paper proposes to explicitly model multi-node proximities which can be widely observed in practice, e.g., multiple researchers coauthor a paper, and multiple genes co-express a protein. Explicitly modeling multi-node proximities is important because some two-node interactions may not come into existence without a third node. By proving that LINE(1st), a recent network embedding method, is equivalent to kernelized matrix factorization, this paper proposes coupled kernelized multi-dimensional array factorization (Cetera) which jointly factorizes multiple multi-dimensional arrays by enforcing a consensus representation for each node. In this way, node representations can be more comprehensive and effective, which is demonstrated on three real-world networks through link prediction and multi-label classification.

Group detection is gaining popularity as it enables various applications ranging from marketing to urban planning. The group information is an important social context which could facilitate a more comprehensive behavior analysis. An example is for retailers to determine the right incentive for potential customers. Existing methods use received signal strength indicator (RSSI) to detect co-located people as groups. However, this approach might have difficulties in crowded urban spaces since many strangers with similar mobility patterns could be identified as groups. Moreover, RSSI is vulnerable to many factors like the human body attenuation and thus is unreliable in crowded scenarios. In this work, we propose a behavior-aware group detection system (BaG). BaG fuses people's mobility information and smartphone usage behaviors. We observe that people in a group tend to have similar phone usage patterns. Those patterns could be effectively captured by the proposed feature: number of bursts (NoB). Unlike RSSI, NoB is more resilient to environmental changes as it only cares about receiving packets or not. Besides, both mobility and usage patterns correspond to the same underlying grouping information. The latent associations between them cannot be fully utilized in conventional detection methods like graph clustering. We propose a detection method based on collective matrix factorization to reveal the hidden associations by factorizing mobility information and usage patterns simultaneously. Experimental results indicate BaG outperforms baseline approaches by in F-score. The proposed system could also achieve robust and reliable performance in scenarios with different levels of crowdedness.

The widespread location-aware applications produce a vast amount of spatio-textual data that contains both spatial and textual attributes. To make use of this enriched information for users to describe their preferences for travel routes, we propose a Bounded-Cost Informative Route (BCIR) query to retrieve the routes that are the most textually relevant to the user-specified query keywords subject to a travel cost constraint. BCIR query is particularly helpful for tourists and city explorers to plan their travel routes. We will show that BCIR query is an NP-hard problem. To answer BCIR query efficiently, we propose an exact solution with effective pruning techniques and an approximate solution with performance guarantee. Extensive experiments over real data sets demonstrate that the proposed solutions achieve the expected performance.

Group gender is essential in understanding social interaction and group dynamics. With the increasing privacy concerns of studying face-to-face communication in natural settings, many participants are not open to raw audio recording. Existing voice-based gender identification methods rely on acoustic characteristics caused by physiological differences and phonetic differences. However, these methods might become ineffective with privacy-sensitive audio for two main reasons. First, compared to raw audio, privacy-sensitive audio contains significantly fewer acoustic features. Moreover, natural settings generate various uncertainties in the audio data. In this paper, we make the first attempt to identify group gender using privacy-sensitive audio. Instead of extracting acoustic features from privacy-sensitive audio, we focus on conversational features including turn-taking behaviors and interruption patterns. However, conversational behaviors are unstable in gender identification as human behaviors are affected by many factors like emotion and environment. We utilize ensemble feature selection and a two-stage classification to improve the effectiveness and robustness of our approach. Ensemble feature selection could reduce the risk of choosing an unstable subset of features by aggregating the outputs of multiple feature selectors. In the first stage, we infer the gender composition (mixed-gender or same-gender) of a group which is used as an additional input feature for identifying group gender in the second stage. The estimated gender composition significantly improves the performance as it could partially account for the dynamics in conversational behaviors. According to the experimental evaluation of 100 people in 273 meetings, the proposed method outperforms baseline approaches and achieves an F1-score of 0.77 using linear SVM.

There are increasing interests in learning low-dimensional and dense node representations from the network structure which is usually high-dimensional and sparse. However, most existing methods fail to consider semantic meanings of links. Different links may have different semantic meanings because the similarities between two nodes can be different, e.g., two nodes share common neighbors and two nodes share similar interests which are demonstrated in node-generated content. In this paper, the former type of links are referred to as structure-close links while the latter type are referred to as content-close links. These two types of links naturally indicate there are two types of characteristics that nodes expose in a social network. Hence, we propose to learn two representations for each node, and render each representation responsible for encoding the corresponding type of node characteristics, which is achieved by jointly embedding the network structure and inferring the type of each link. In the experiments, the proposed method is demonstrated to be more effective than five recent methods on four social networks through applications including visualization, link prediction and multi-label classification.

Nowadays, there is an emerging way of connecting logistics orders and van drivers, where it is crucial to predict the order response time. Accurate prediction of order response time would not only facilitate decision making on order dispatching, but also pave ways for applications such as supply-demand analysis and driver scheduling, leading to high system efficiency. In this work, we forecast order response time on current day by fusing data from order history and driver historical locations. Specifically, we propose Coupled Sparse Matrix Factorization (CSMF) to deal with the heterogeneous fusion and data sparsity challenges raised in this problem. CSMF jointly learns from multiple heterogeneous sparse data through the proposed weight setting mechanism therein. Experiments on real-world datasets demonstrate the effectiveness of our approach, compared to various baseline methods. The performances of many variants of the proposed method are also presented to show the effectiveness of each component.

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.

As there are various data mining applications involving network analysis, network embedding is frequently employed to learn latent representations or embeddings that encode the network structure. However, existing network embedding models are only designed for a single network scenario. It is common that nodes can have multiple types of relationships in big data era, which results in multiple networks, e.g., multiple social networks and multiple gene regulatory networks. Jointly embedding multiple networks thus may make network-specific embeddings more comprehensive and complete as the same node may expose similar or complementary characteristics in different networks. In this paper, we thus propose an idea of multi-task network embedding (MTNE) to jointly learn multiple network-specific embeddings for each node via enforcing an extra information-sharing embedding. Moreover, we instantiate the idea in two models that are different in the mechanism for enforcing the information-sharing embedding. The first model enforces the information-sharing embedding as a common embedding shared by all tasks, which is similar to the concept of the common metric in multi-task metric learning while the second model enforces the information-sharing embedding as a consensus embedding on which all network-specific embeddings agree. We demonstrate through comprehensive experiments on three real-world datasets that the proposed models outperform state-of-the-art network embedding models in applications including visualization, link prediction, and multi-label classification.

Network embedding has been increasingly employed in networked data mining applications as it is effective to learn node embeddings that encode the network structure. Existing network models usually learn a single embedding for each node. In practice, a person may interact with others in different roles, such as interacting with schoolmates as a student, and with colleagues as an employee. Obviously, different roles exhibit different characteristics or features. Hence, only learning a single embedding responsible for all roles is not appropriate. In this paper, we thus introduce a concept of multiple social role (MSR) into social network embedding for the first time. The MSR models multiple roles people play in society, such as student and employee. To make the embedding more versatile, we thus propose a multiple social role embedding (MSRE) model to preserve both the network structure and social roles. Empirical evaluation on various real-world social networks demonstrates advantages of the proposed MSRE over the state-of-the-art embedding models in link prediction and multi-label classification.

Link prediction is an important and interesting application for social networks because it can infer potential links among network participants. Existing approaches basically work with the homophily principle, i.e., people of similar characteristics tend to befriend each other. In this way, however, they are not suitable for inferring negative links or hostile links, which usually take place among people with different characteristics. Moreover, negative links tend to couple with positive links to form signed networks. In this paper, we thus study the problem of disentangled link prediction (DLP) for signed networks, which includes two separate tasks, i.e., inferring positive links and inferring negative links. Recently, representation learning methods have been proposed to solve the link prediction problem because the entire network structure can be encoded in representations. For the DLP problem, we thus propose to disentangle a node representation into two representations, and use one for positive link prediction and another for negative link prediction. Experiments on three real-world signed networks demonstrate the proposed disentangled representation learning (DRL) method significantly outperforms alternatives in the DLP problem.

Network embedding is increasingly employed to assist network analysis as it is effective to learn latent features that encode linkage information. Various network embedding methods have been proposed, but they are only designed for a single network scenario. In the era of big data, different types of related information can be fused together to form a coupled heterogeneous network, which consists of two different but related sub-networks connected by inter-network edges. In this scenario, the inter-network edges can act as comple- mentary information in the presence of intra-network ones. This complementary information is important because it can make latent features more comprehensive and accurate. And it is more important when the intra-network edges are ab- sent, which can be referred to as the cold-start problem. In this paper, we thus propose a method named embedding of embedding (EOE) for coupled heterogeneous networks. In the EOE, latent features encode not only intra-network edges, but also inter-network ones. To tackle the challenge of heterogeneities of two networks, the EOE incorporates a harmonious embedding matrix to further embed the em- beddings that only encode intra-network edges. Empirical experiments on a variety of real-world datasets demonstrate the EOE outperforms consistently single network embedding methods in applications including visualization, link prediction multi-class classification, and multi-label classification.

Wireless networks offer many advantages over wired local area networks such as scalability and mobility. Strategically deployed wireless networks can achieve multiple objectives like traffic offloading, network coverage, and indoor localization. To this end, various mathematical models and optimization algorithms have been proposed to find optimal deployments of access points (APs). However, wireless signals can be blocked by the human body, especially in crowded urban spaces. As a result, the real coverage of an on-site AP deployment may shrink to some degree and lead to unexpected dead spots (areas without wireless coverage). Dead spots are undesirable, since they degrade the user experience in network service continuity, on one hand, and, on the other hand paralyze some applications and services like tracking and monitoring when users are in these areas. Nevertheless, it is nontrivial for existing methods to analyze the impact of human beings on wireless coverage. Site surveys are too time consuming and labor intensive to conduct. It is also infeasible for simulation methods to predict the number of on-site people. In this article, we propose DMAD, a Data-driven Measuring of Wi-Fi Access point Deployment, which not only estimates potential dead spots of an on-site AP deployment but also quantifies their severity, using simple Wi-Fi data collected from the on-site deployment and shop profiles from the Internet. DMAD first classifies static devices and mobile devices with a decision-tree classifier. Then it locates mobile devices to grid-level locations based on shop popularities, wireless signal, and visit duration. Last, DMAD estimates the probability of dead spots for each grid during different time slots and derives their severity considering the probability and the number of potential users. The analysis of Wi-Fi data from static devices indicates that the Pearson Correlation Coefficient of wireless coverage status and the number of on-site people is over 0.7, which confirms that human beings may have a significant impact on wireless coverage. We also conduct extensive experiments in a large shopping mall in Shenzhen. The evaluation results demonstrate that DMAD can find around 70% of dead spots with a precision of over 70%.

The widespread location-aware applications produce a vast amount of spatio-textual data that contains both spatial and textual attributes. To make use of this enriched information for users to describe their preferences for travel routes, we propose a Bounded-Cost Informative Route (BCIR) query to retrieve the routes that are the most textually relevant to the user-specified query keywords subject to a travel cost constraint. BCIR query is particularly helpful for tourists and city explorers to plan their travel routes. We will show that BCIR query is an NP-hard problem. To answer BCIR query efficiently, we propose an exact solution with effective pruning techniques and two approximate solutions with performance guarantees. Extensive experiments over real data sets demonstrate that the proposed solutions achieve the expected performance.

In an IMANET, mobile users access both text and media web contents on the Internet through gateway nodes, with web access patterns, i.e., the Zipf-like distribution or the Stretched Exponential distribution. To reduce data access delay from the Internet, we consider the cache placement problem in cooperative caching, which is that each mobile node selects a subset of web contents to cache cooperatively in its limited cache so that total access cost is minimized. It has been proved NP-hard. We propose a solution named Adaptive Allocation Cooperative Caching (AACC), which adaptively divides the cache space of each node into two components: altruistic and selfish, according to detected data access patterns. AACC aims to find the optimal cache allocation solution to allocate appropriate cache spaces for two components in order to minimize total access cost. Given the Zipf-like access distribution, we find a near-optimal allocation solution to the cache placement problem. Simulation results show that AACC achieves much better performance than the existing best cooperative caching strategy in IMANETs in terms of average query delay, caching overheads, and query success ratio. In particular, AACC reduces caching overheads by 40% in average.

With the increasing popularity of the peer-to-peer (P2P) computing paradigm, many general range query schemes for distributed hash table (DHT)-based P2P systems have been proposed in recent years. Although those schemes can provide range query capability without modifying the underlying DHTs, they have the query delay depending on both the scale of the system and the size of the query space or the specific query, and thus cannot guarantee to return the query results in a bounded delay. In this paper, we propose Armada, an efficient range query processing scheme to support delay-bounded single-attribute and multiple-attribute range queries. It is the first delay-bounded general range query scheme on constant-degree DHTs, and can return the results for any range query within 2logN hops in a P2P system with N peers. Results of analysis and simulations show that the average delay in Armada is less than logN, and the average message cost of single-attribute range queries is about logN+2n 2 (n is the number of peers that intersect with the query). These results are very close to the lower bounds on delay and message cost of range queries over constant-degree DHTs.

In wireless sensor networks, energy is the most crucial resource. In-network data processing is a common technique in which an intermediate proxy node is chosen to house a possibly complicated data transformation function to consolidate the sensor data streams from the source nodes, en route to the sink node. We investigate into the placement problem of the proxy. We formulate and solve the energy minimization problem analytically, based on an ENergy- Efficient Rate-Governed Yardstick (ENERGY). An optimal solution is derived based on complete network topology information. Taking into account realistic sensor network constraints that only neighboring network connectivity is known to a node, we develop an approximate but effective solution, ENERGY . We evaluate the performance of ENERGY, which performs well even in low-density networks and for queries requesting from data sources at a distance.

This paper describes a dynamic service reconfiguration model where the proxy is composed of a chain of service objects called mobilets (pronounced as mo-be-lets), which can be deployed onto the network actively. This model offers flexibility because the chain of mobilets can be dynamically reconfigured to adapt to the vigorous changes in the characteristics of the wireless environment, without interrupting the service provision for other mobile nodes. Furthermore, mobilets can also be migrated to a new proxy server when the mobile node moves to a different network domain. We have realized the dynamic service reconfiguration model by crafting its design into a programmable infrastructure that forms the baseline architecture of the WebPADS (short for Web Proxy for Actively Deployable Services) system.

Spatial data, ranging from various land information data to different types of environmental data, are typically collected and used by different custodians. The full benefits of using spatial data can be achieved by combining the data from different sources covering a common region. Due to organizational, political and technical reasons, it is unrealistic to physically integrate the vast amount of spatial data managed by different systems in different organizations. A practical approach is to provide interoperability to support multi-site data queries. In this paper, we study the performance aspect of complex spatial query processing. We propose a framework for processing queries with multiple spatial and aspatial predicates using data from multiple sites. Using a new concept called generalized filter, a query is processed in three steps. First, an aspatial filter that incorporates some conditions derived from spatial predicates is used to find a set of candidates, which is a superset of the final query results. Then, the candidates are manipulated and a refinement step is executed following an optimized candidate sequence. Finally, a post-processing step is used to handle spatial expressions in query results. The focus of this paper is to generate enhanced filters in order to minimize the need of transferring and processing complex spatial data.