Graph Neural Networks (GNNs) are vulnerable to graph backdoor attacks, which poses severe risks to their deployment in safety-critical applications. Existing defenses predominantly focus on specific backdoor triggers, making them brittle and unable to generalize across different backdoor triggers with varying properties. Motivated by this limitation, this work proposes an attack-agnostic graph backdoor defense mechanism A2GBD, which does not require prior knowledge of the specific attack strategies (e.g., edge perturbation, node attribute manipulation) to achieve effective defense. A2GBD consists of suspicious node selection and defense strategy generation. The selection module selects high-suspicion nodes to enhance defense awareness, while the defense agent adaptively determines and executes defense strategies. Extensive experiments on multiple benchmark datasets demonstrate that A2GBD consistently lowers attack success rates while maintaining high clean accuracy, showing strong robustness and generalizability against diverse graph backdoor attack strategies.

Traffic forecasting is pivotal but challenging due to intricate spatio-temporal dynamics. Existing models often apply a uniform spatial mechanism across distinct temporal scales and rely on static feature embeddings. Consequently, they are inadequate in capturing scale-specific spatial heterogeneity and dynamic feature interdependencies. To address these limitations, we propose the Multi-Scale Spatio-Temporal Attention Network (MSSTAN) with a novel dual-branch architecture: (1) A Global-Local Feature Attention Network (GLFAN) that explicitly decouples spatial interactions across decomposed temporal components to capture multi-scale spatial patterns; and (2) A Spatio-Temporal Feature Attention Network (STFAN) that dynamically recalibrates feature importance based on specific spatio-temporal contexts. A dynamic branch fusion mechanism integrates these branches to optimally aggregate their complementary views. Extensive experiments on five real-world datasets demonstrate that MSSTAN achieves state-of-the-art or highly competitive performance, validating its efficacy for traffic forecasting.

Multi-party urban flow analysis is a crucial task in smart cities. However, existing analysis methods has difficulty in trade-off between data privacy security and spatio-temporal feature capture. The solution to the problem of how to capture the complete spatio-temporal features of multi-party urban flow data while protecting data privacy is of great importance in multi-party urban flow analysis. Therefore, to address data privacy and spatio-temporal feature capture in multi-party urban flow analysis, this paper proposes a spatio-temporal federated analysis model, for multi-party urban flow mining, which is able to effectively protect data privacy and capture spatio-temporal features completely at the same time. Firstly, a multi-party urban flow mining framework based on federated learning is proposed to realize complete capture of spatio-temporal feature information of multi-party urban flow data and mining urban flow pattern knowledge under the premise of protecting data privacy. Secondly, to address the communication cost of the multi-party urban flow analysis, we propose a lazy aggregation method based on similarity clustering, which improves the communication efficiency between clients and the server. Further, we propose a similarity evaluation criteria for urban flow data based on step function, which can effectively calculate the similarity between urban flow data. Finally, we compare the proposed model with some benchmark methods on Chengdu Didi order data and point of interest data to prove the effectiveness of the proposed model and visualize and analyze the spatio-temporal features.

Time series forecasting has become a critical task in data engineering, with the volume of time series data projected to reach 180 ZB by 2025. While traditional forecasting models are typically constrained to single domains, missing opportunities for transferring temporal patterns across different domains. Through analysis, we observe that time series from different domains, despite their distinct statistical characteristics, can be fundamentally understood through temporal dependency patterns, which manifest as either long-term dependencies ( like trends and cycles) or short-term dependencies ( like fluctuations and abrupt changes). This observation motivates us to rethink cross-domain modeling from the dependency preferences perspective. We propose LSTPO, a novel framework that captures cross-domain commonalities through temporal dependency preferences and leverages a meta-learning-based approach to prevent cross-domain training forgetting. LSTPO dynamically models changes in preference over time and swiftly adapts to preference variations across different domains, enabling robust cross-domain forecasting. Through extensive experimental evaluations, we have shown that LSTPO substantially outperforms state-of-the-art forecasting methods while enhancing model transferability under few-shot learning conditions. The source code will be made publicly available upon acceptance.

Unsupervised feature selection aims to enhance the quality of unlabeled data, thereby improving the performance of subsequent unsupervised learning models. However, most of the existing unsupervised feature selection methods rely on single-granularity modeling, which reduces the expressive capability of data to some extent. In addition, the existing studies are generally based on a forward greedy feature selection strategy, which tends to fall into a local optimum. To address these issues, this paper proposes a novel unsupervised feature selection method for handling hybrid data, called unsupervised feature selection method using fuzzy graph momentum random walk in bi-level granular-ball knowledge space. Specifically, a Bi-level Granular-ball Knowledge Space (BGKS) is first constructed by combining fine granularity and coarse granularity representations through a hybrid Gaussian kernel function. Then, a multi-granularity fuzzy graph is built on the BGKS using upper and lower fuzzy approximation operators. Based on this graph, a Momentum Random Walk (MRW) mechanism is introduced to design the Fuzzy Graph Momentum Random Walk (FGMRW) model. Finally, an iterative unsupervised feature selection algorithm is developed. Extensive experiments on 20 public datasets demonstrate that, compared with existing algorithms, the proposed method is able to maintain or even improve clustering performance while selecting fewer features, thus achieving superior overall performance. The source code of this work is publicly available athttps://github.com/HongtaoGao-code/FGMRW-UFS.

Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction results, we find that data biases simultaneously reduce CL's ability to retain and transfer knowledge. To address this, we propose ErrorEraser, a universal plugin that removes erroneous memories caused by biases in CL, enhancing performance in both new and old tasks. ErrorEraser consists of two modules: Error Identification and Error Erasure. The former learns the probability density distribution of task data in the feature space without prior knowledge, enabling accurate identification of potentially biased samples. The latter ensures only erroneous knowledge is erased by shifting the decision space of representative outlier samples. Additionally, an incremental feature distribution learning strategy is designed to reduce the resource overhead during error identification in downstream tasks. Extensive experimental results show that ErrorEraser significantly mitigates the negative impact of data biases, achieving higher accuracy and lower forgetting rates across three types of CL methods. The code is available at https://github.com/diadai/ErrorEraser.

Open-world continual learning (OWCL) adapts to sequential tasks with open samples, learning knowledge incrementally while preventing forgetting. However, existing OWCL still requires a large amount of labeled data for training, which is often impractical in real-world applications. Given that new categories/entities typically come with limited annotations and are in small quantities, a more realistic situation is OWCL with scarce labeled data, i.e., few-shot training samples. Hence, this paper investigates the problem of open-world few-shot continual learning (OFCL), challenging in (i) learning unbounded tasks without forgetting previous knowledge and avoiding overfitting(ii) constructing compact decision boundaries for open detection with limited labeled data, and (iii) transferring knowledge about knowns and unknowns and even update the unknowns to knowns once the labels of open samples are learned. In response, we propose a novel OFCL framework that integrates three key components: (1) an instance-wise token augmentation (ITA) that represents and enriches sample representations with additional knowledge(2) a margin-based open boundary (MOB) that supports open detection with new tasks emerge over time, and (3) an adaptive knowledge space (AKS) that endows unknowns with knowledge for the updating from unknowns to knowns. Finally, extensive experiments show that the proposed OFCL framework outperforms all baselines remarkably with practical importance and reproducibility. The source code is released at https://github.com/liyj1201/OFCL.

Multivariate Time Series Forecasting (MTSF) has long been a key research focus. Traditionally, these studies assume a fixed number of variables, but in real-world applications, Cyber-Physical Systems often expand as new sensors are deployed, increasing variables in MTSF. In light of this, we introduce a novel task, Expanding-variate Time Series Forecasting (EVTSF). This task presents unique challenges, specifically (1) handling inconsistent data shapes caused by adding new variables, and (2) addressing imbalanced spatio-temporal learning, where expanding variables have limited observed data due to the necessity for timely operation. To address these challenges, we propose STEV, a flexible spatio-temporal forecasting framework. STEV includes a new Flat Scheme to tackle the inconsistent data shape issue, which extends the graph-based spatio-temporal modeling architecture into 1D space by flattening the 2D samples along the variable dimension, making the model variable-scale-agnostic while still preserving dynamic spatial correlations through a holistic graph. Additionally, we introduce a novel Spatio-temporal Focal Learning strategy that incorporates a negative filter to resolve potential conflicts between contrastive learning and graph representation, and a focal contrastive loss as its core to guide the framework to focus on optimizing the expanding variables. To evaluate the effectiveness of STEV, we benchmark EVTSF performance on three real-world datasets from various domains and compare it against three potential solutions employing state-of-the-art (SOTA) MTSF models tailored for EVSTF. Experimental results show that STEV significantly outperforms its competitors, especially in handling expanding variables. Notably, STEV, with only 5% of observations during the expanding period, is on par with SOTA MTSF models trained with complete data. Further exploration of various expanding scenarios underscores the generalizability of STEV in real-world applications.

Sarcasm is a subtle expression that indicates the incongruity between literal meanings and factual opinions. For multimodal posts in social medias which consist of both images and texts, sarcasm expressions are even more widespread. Recent works have paid attentions to Multimodal Sarcasm Target Identification (MSTI), which focuses on detecting aspect terms of mockery or ridicule as sarcasm targets. However, the current MSTI benchmark only contains annotations on fine-grained sarcasm targets within sarcastic samples. In practice, it will be featured by two major limitations. First, there lack annotations on non-sarcasm aspects to inform deep models to perceive the semantic difference between sarcasm targets and non-sarcasm aspects. As a result, deep models will tend to incorrectly recognize non-sarcasm aspects as sarcasm targets. Second, there lack non-sarcasm samples to inform deep models to perceive the inherent semantics of sarcasm intentions. Due to the subtle characteristic of sarcasm expressions, models trained with only fine-grained supervision signals cannot thoroughly understand the sarcasm semantics, making the fine-grained task of sarcasm target identification restricted. Motivated by these limitations, this work reconstructs a more comprehensive MSTI benchmark by introducing both fine-grained non-sarcasm aspect annotations for existing sarcasm samples and non-sarcastic samples as non-sarcasm references to enable deep models to clearly perceive the mentioned information during training. Based on the multi-granularity (i.e., both aspect-level and sample-level) non-sarcasm information introduced into this new benchmark, this work further proposes a pluggable Semantics-aware Sarcasm Target Identification mechanism to enhance sarcasm target identification by modeling the overall semantics of sarcasm intentions via an auxiliary sample-level sarcasm recognition task. By modeling the overall semantics of sarcasm intention, deep models can obtain a more comprehensive understanding on sarcasm semantics, leading to improved performance on fine-grained sarcasm target identification. Extensive experiments are conducted to validate our contribution. Both the dataset and code are available at https://github.com/tiggers23/MSTI-Plus.

Air quality modeling and forecasting has become a key problem in environmental protection. The existing prediction models typically require large‐scale and high‐quality historical data to achieve better performance. However, insufficient data volume and significant differences between data distribution across different regions will definitely reduce the effectiveness of the model reuse. To address the above issues, we propose a novel hybrid recurrent network based on domain adversarial transfer to achieve a stronger generalization ability when training air quality data from multisource domains. The proposed model mainly consists of three fundamental modules, i.e., feature extractor, regression predictor, and domain classifier. One‐dimensional convolutional neural networks (1D‐CNNs) are used to extract temporal feature of data from source and target stations. Bi‐directional gated recurrent unit (bi‐GRU) and bi‐directional long short‐term memory (bi‐LSTM) are utilized to learn temporal dependencies pattern of multivariate time series data. Two adversarial transfer strategies are employed to ensure that our model is capable of finding domain invariant representations automatically. Experiments with different number of source domains are conducted to demonstrate the effectiveness of the proposed domain transfer strategies. The experimental results also show that our composite model has superior performance for forecasting air quality in various regions. As further evidence, the adversarial training method could promote the positive transfer and alleviate the negative effect of irrelevant source data. Besides, our model exhibits preferable generalization capability as more robust prediction results are achieved on both unseen target domains and original source domains.

Temporal data analysis plays a pivotal role in applications such as weather forecasting, traffic flow management, energy consumption monitoring, and other areas of urban computing. In recent years, temporal data modeling has transitioned from traditional deep learning methods to pre-trained models. However, existing approaches often exhibit significant task-specific limitations, requiring bespoke model designs and extensive domain data for training. To address these challenges, this study introduces KPT, a novel foundation model for temporal data analysis in urban computing. By leveraging temporal competitive attention and feature interaction attention mechanisms, KPT can effectively capture global context, integrate cross-variable features precisely, and achieve universal feature learning across diverse time series tasks. Additionally, the knowledge prompt network facilitates the deep fusion of cross-layer features via an intricate interaction mechanism, enabling the model to identify and align shared temporal patterns across different time series data. These patterns then transformed into knowledge prompts, thereby enhancing the universal feature learning capabilities of the pre-trained model. Experimental results demonstrate that KPT excels in four core temporal analysis tasks within urban computing, outperforming task-specific models. This highlights KPT’s ability to generalize across tasks and underscores its potential as a foundation model for multi-task scenarios in urban computing.

Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by1.47%to9.19%and change value errors by4.57%to15.78%, highlighting its substantial performance improvements.

The effective management of trajectory data heavily relies on the utilization of fundamental spatio-temporal queries. The surge in trajectory data, with its dynamic spatio-temporal properties, poses notable management challenges. Existing systems are inadequate in providing fine-grained trajectory representations and efficient architecture for processing queries, leading to significant computational overhead. This paper introduces TMan to address these challenges. First, TMan presents two innovative index structures that precisely capture the spatio-temporal characteristics of trajectory data. Compared to the state-of-the-art indexes, our indexes for temporal range and spatial range queries can reduce the number of retrievals by up to 77% and 83%, respectively. Next, TMan devises concise and effective encoding methods for these indexes. Leveraging these indexes, TMan provides a distributed storage structure and an index caching mechanism for efficiently managing trajectories in key-value data stores. Moreover, TMan introduces a parallel query processing approach incorporating a push-down strategy to enhance the efficiency of fundamental queries. Extensive experimental results demonstrate that TMan's index structures and architecture outperform the baselines.

Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on predefined or self-learning graphs, which are either static or unintentionally dynamic, and thus cannot model the time-varying correlations that exhibit trends and periodicities caused by the regularity of the underlying processes in CPS. To tackle such limitation, we propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. Notably, we introduce time discrepancy learning that utilizes contrastive learning with distance-based regularization terms to constrain learned spatial correlations to a trend sequence. Additionally, we propose a periodic discriminant function to enable the capture of periodic changes from the state of nodes. Next, we present a Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures spatial and temporal dependencies while learning time-aware and node-specific patterns. Finally, we introduce a unified framework named Time-aware Graph Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an encoder-decoder architecture for multi-step spatiotemporal forecasting. We report on experiments with TGCRN and popular existing approaches on five real-world datasets, thus providing evidence that TGCRN is capable of advancing the state-of-the-art. We also cover a detailed ablation study and visualization analysis, offering detailed insight into the effectiveness of time-aware structure learning.

Indoor temperature prediction is crucial for decision-making in central heating systems. Beyond accuracy, predictions shall be interpretable, i.e. conform to the laws of physics; otherwise, it may lead to system failures or unsafe conditions. However, deep learning models often face criticism regarding interpretability, which limits their application in such settings. To this end, we propose a Spatio-Temporal Consistency enhanced Differential Network (CONST) for interpretable indoor temperature prediction. Our approach mainly consists of a differential predictive module and a spatio-temporal consistency module. Modeling the influential factors, the first module solves the issue of multicollinearity through the differential operation. Considering the heterogeneity of global and local data distributions, the second module characterizes the temporal and spatial consistency to mine the universal pattern by multi-task learning, thereby improving the prediction interpretability. Besides, we propose a set of interpretability metrics to overcome the drawbacks of partial dependence plot metric, which are more practical, zero-centered, flexible, and numerical. We conclude experiments on a real-world dataset with four heating stations. The results demonstrate the advantages of our approach over various baselines, where the interpretability can be improved by more than 8 times on cRPD while maintaining high accuracy. We developed CONST on the SmartHeat system, providing hourly indoor temperature forecasts for 13 heating stations in northern China.

Personalized Federated Continual Learning (PFCL) is a new practical scenario that poses greater challenges in sharing and personalizing knowledge. PFCL not only relies on knowledge fusion for server aggregation at the global spatial-temporal perspective but also needs model improvement for each client according to the local requirements. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and adopt generalized knowledge to itself by coarse-to-fine human cognitive mechanisms. Moreover, it allows more effectively to personalized shared knowledge, thus serving its own purpose. To this end, we propose a novel concept called multi-granularity prompt, i.e., coarse-grained global prompt acquired through the common model learning process, and fine-grained local prompt used to personalize the generalized representation. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. In addition, we design a selective prompt fusion mechanism for aggregating knowledge of global prompts distilled from different clients. By the exclusive fusion of coarse-grained knowledge, we achieve the transmission and refinement of common knowledge among clients, further enhancing the performance of personalization. Extensive experiments demonstrate the effectiveness of the proposed method in addressing STCF as well as improving personalized performance.

Abstract With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is common, traffic accidents happen frequently, and traffic environments are deteriorating. To alleviate these issues and improve the efficiency of urban transportation, accurate traffic forecasting is crucial. In this study, we aim to provide a comprehensive overview of the overall architecture of traffic forecasting, covering aspects such as traffic data analysis, traffic data modeling, and traffic forecasting applications. We begin by introducing existing traffic forecasting surveys and preliminaries. Next, we delve into traffic data analysis from traffic data collection, traffic data formats, and traffic data characteristics. Additionally, we summarize traffic data modeling from spatial representation, temporal representation, and spatio-temporal representation. Furthermore, we discuss the application of traffic forecasting, including traffic flow forecasting, traffic speed forecasting, traffic demand forecasting, and other hybrid traffic forecasting. To support future research in this field, we also provide information on open datasets, source resources, challenges, and potential research directions. As far as we know, this paper represents the first comprehensive survey that focuses specifically on the overall architecture of traffic forecasting.

Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE , a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms.

Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical problem of SNMF is that usually there is no learning rate guidance during the optimization process, which often leads to a poor representation ability. To overcome this limitation, we propose a very general representation learning framework (DNSRF) that is based on a deep neural net. Essentially, the parameters of the deep net used to construct the DNSRF algorithms are obtained by matrix element update. In combination with different activation functions, DNSRF can be implemented in various ways. In our experiments, we tested nine instances of our DNSRF framework on six benchmark datasets. In comparison with other state-of-the-art methods, the results demonstrate the superior performance of our framework, which is thus shown to have a great representation ability.

Co-clustering is to cluster samples and features simultaneously, which can also reveal the relationship between row clusters and column clusters. Therefore, lots of scientists have drawn much attention to conduct extensive research on it, and co-clustering is widely used in recommendation systems, gene analysis, medical data analysis, natural language processing, image analysis, and social network analysis. In this article, we survey the entire research aspect of co-clustering, especially the latest advances in co-clustering, and discover the current research challenges and future directions. First, due to different views from researchers on the definition of co-clustering, this article summarizes the definition of co-clustering and its extended definitions, as well as related issues, based on the perspectives of various scientists. Second, existing co-clustering techniques are approximately categorized into four classes: information-theory-based, graph-theory-based, matrix-factorization-based, and other theories-based. Third, co-clustering is applied in various aspects such as recommendation systems, medical data analysis, natural language processing, image analysis, and social network analysis. Furthermore, 10 popular co-clustering algorithms are empirically studied on 10 benchmark datasets with 4 metrics—accuracy, purity, block discriminant index, and running time, and their results are objectively reported. Finally, future work is provided to get insights into the research challenges of co-clustering.

Competitor identification is an essential component of corporate strategy. With the rapid development of artificial intelligence, various data-mining methodologies and frameworks have emerged to identify competitors. In general, the competitiveness among companies is determined by both market commonality and resource similarity. However, because resource information is more difficult to obtain than market information, existing studies primarily identify competitors via market commonality. To address this limitation, we introduce multisource company descriptions as well as heterogeneous business relationships, and we propose a novel method for simultaneously mining the market commonality and resource similarity. First, we use multisource company descriptions to represent companies and transform the heterogeneous business relationships into a heterogeneous business network. Then, we propose a novel multisource data fusion-based heterogeneous graph attention network (MHGAT) to learn the pairwise competitive relationships between companies. Specifically, a graph neural network-based model is proposed to learn the embeddings of companies by preserving their competition, and a multilevel attention framework is designed to integrate the embeddings from neighboring company level, heterogeneous relationship level, and multisource description level. Finally, experiments on a real-world dataset verify the effectiveness of our proposed MHGAT and demonstrate the usefulness of company descriptions and business relationships in competitor identification.

This paper presents a novel framework for continual feature selection (CFS) in data preprocessing, particularly in the context of an open and dynamic environment where unknown classes may emerge. CFS encounters two primary challenges: the discovery of unknown knowledge and the transfer of known knowledge. To this end, we propose a GBCFS method, which combines the strengths of continual learning (CL) with granular-ball computing (GBC). The GBCFS method focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection. GBCFS consists of two stages: initial learning and open learning. The former aims to establish an initial knowledge base through multi-granularity representation using granular balls. The latter utilizes prior granular-ball knowledge to identify unknowns, updates the knowledge base for granular-ball knowledge transfer, reinforces old knowledge, and integrates new knowledge. Subsequently, we devise an optimal feature subset mechanism that incorporates minimal new features into the existing optimal subset, often yielding superior results during each period. Extensive experimental results on public benchmark datasets demonstrate our method's superiority in terms of both effectiveness and efficiency compared to state-of-the-art feature selection methods.

Fraud detection poses a fundamental yet challenging problem to mitigate various risks associated with fraudulent activities. However, existing methods are limited by their reliance on static data within single geographical regions, thereby restricting the trained model’s adaptability across different regions. Practically, when enterprises expand their business into new cities or countries, training a new model from scratch can incur high computational costs and lead to catastrophic forgetting (CF). To address these limitations, we propose cross-regional fraud detection as an incremental learning problem, enabling the development of a unified model capable of adapting across diverse regions without suffering from CF. Subsequently, we introduce Cross-Regional Continual Learning (CCL), a novel paradigm that facilitates knowledge transfer and maintains performance when incrementally training models from previously learned regions to new ones. Specifically, CCL utilizes prototype-based knowledge replay for effective knowledge transfer while implementing a parameter smoothing mechanism to alleviate forgetting. Furthermore, we construct heterogeneous trade graphs (HTGs) and leverage graph-based backbones to enhance knowledge representation and facilitate knowledge transfer by uncovering intricate semantics inherent in cross-regional datasets. Extensive experiments demonstrate the superiority of our proposed method over baseline approaches and its substantial improvement in cross-regional fraud detection performance.

Learning classification models from real-world data often requires substantial human effort devoted to instance annotation. As the instance-based annotating process can be very time-consuming and costly, we propose a novel active learning framework that builds classification models from human-annotatedregions. A region is defined by a set of conjunctive patterns that are formed by value ranges over the input features. A region label is a human assessment of the classproportionin the data population covered by the region. By leveraginglearning from label proportionsalgorithms, regions and their class proportions can be used to train instance-based classification models. However, the key challenge is that in practice, very few regions are defined already. Therefore, to identify regions important for model learning, we design ahierarchical active learning(HAL) framework, which actively builds a hierarchy of regions. Similar to the decision-tree learning process, our approach progressively divides the input data space into smaller sub-regions, solicits labels for the new regions, and retrains the base classification model with all the leaf regions. And we further develop amulti-hierarchy(forest) solution, which builds multiple shallower hierarchies that have more informative, diverse, and simpler regions. We evaluate our HAL framework on numerous impactful classification datasets as well as on a real user study - on the survival analysis of colorectal cancer patients. The results demonstrate that region-based active learning methods can learn high-quality classifiers from very few labeled regions. Hence, our framework is shown very effective in reducing the human annotation effort needed for building classification models.

With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of three parts: 1) Methodology-wise. We categorize a large number of basic CL methods and advanced CL frameworks in combination with other learning paradigms including graph learning, spatial-temporal learning, multi-modal learning, and federated learning. 2) Application-wise. We present numerous CL applications covering transportation, environment, public health, safety, networks, and associated datasets related to urban computing. 3) Challenges. We discuss current problems and challenges and envision several promising research directions. We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.

Data privacy and silos are nontrivial and greatly challenging in many real-world applications. Federated learning is a decentralized approach to training models across multiple local clients without the exchange of raw data from client devices to global servers. However, existing works focus on a static data environment and ignore continual learning from streaming data with incremental tasks. Federated Continual Learning (FCL) is an emerging paradigm to address model learning in both federated and continual learning environments. The key objective of FCL is to fuse heterogeneous knowledge from different clients and retain knowledge of previous tasks while learning on new ones. In this work, we delineate federated learning and continual learning first and then discuss their integration, i.e., FCL, and particular FCL via knowledge fusion. In summary, our motivations are four-fold: we (1) raise a fundamental problem called “spatial-temporal catastrophic forgetting” and evaluate its impact on the performance using a well-known method called federated averaging (FedAvg), (2) integrate most of the existing FCL methods into two generic frameworks, namely synchronous FCL and asynchronous FCL, (3) categorize a large number of methods according to the mechanism involved in knowledge fusion, and finally (4) showcase an outlook on the future work of FCL.

Feature learning is a crucial phase machine learning [1] – [3] . How to obtain appropriate features distribution without any background is still a hard problem in machine learning. In this paper, we present a novel unsupervised feature learning architecture (see Fig. 1 ), which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we choose three clusterers to obtain three different global clustering partitions (CPs). Then, an unanimous voting strategy is used to generate the local clustering partition (LCP) of visible layer data. Hence, the LCP only has partial visible layer data. The novel MIRBM model is a core feature encoding part of the proposed unsupervised feature learning architecture. The novelty of it is that the LCP as an unsupervised guidance is integrated into the CD 1 learning to guide the distribution of the hidden layer features. For the instance in the same LCP cluster, the hidden and reconstructed hidden layer features of the MIRBM model in the proposed architecture tend to constrict together in the training process. Meanwhile, each LCP center tends to disperse from each other as much as possible in the hidden and reconstructed hidden layer during training. This work has three main contributions: 1) A novel unsupervised feature learning architecture is proposed, which consists of a multi-clustering integration module and an MIRBM model. 2) In the multi-clustering integration module of the proposed architecture, three unsupervised algorithms are employed to obtain three different global CPs without any background knowledge or label. 3) The MIRBM model in the proposed architecture uses the LCP as an unsupervised guidance to guide the distribution of the hidden layer features by integrating the LCP into the CD 1 learning.

Stay area detection is one of the most important applications in trajectory data mining, which is helpful to understand human's behavior intentions. Traditional stay area detection methods are based on GPS data with relatively high sampling rate. However, because of privacy issues, accessing GPS data can be difficult in most real-world applications. Fortunately, traffic surveillance cameras have been widely deployed in urban area, and it provides us a novel way of acquiring vehicles' trajectories. All the vehicles that traverse by can be recognized and recorded in a passive way. However, the trajectory data collected in this way is extremely coarse, because the surveillance cameras are only deployed in important locations, such as crossroads. This coarse trajectory introduces two challenges for the stay area detection problem, i.e., whether and where the stay event occurs. In this paper, we design a two-stage method to solve the stay area detection problem with coarse trajectories. It first detects the stay event between a surveillance camera record pair, then uses a layer-by-layer stay area identification algorithm to infer the exact stay area. Extensive experiments based on real-world data were used to evaluate the performance of the proposed framework. Results demonstrate the proposed framework SAInf achieved a 58% performance improvement compared with SOTA methods.

Representation learning based on autoencoders has received great concern for its potential ability to capture valuable latent information. Conventional autoencoders pursue minimal reconstruction error, but in most machine learning tasks such as classification and clustering, the discrimination of feature representation is also important. To address this limitation, an enhanced self-supervised discriminative fuzzy autoencoder (FAE) is innovatively proposed, which focuses on exploring information within data to guide the unsupervised training process and enhancing feature discrimination in a self-supervised manner. In FAE, fuzzy membership is applied to provide a means of self-supervised, which allows FAE can not only utilize AE’s outstanding representation learning capabilities but can also transform the original data into another space with improved discrimination. First, the objective function corresponding to FAE is proposed by reconstruction loss and clustering oriented loss simultaneously. Subsequently, Mini-Batch Gradient Descent is applied to infer the objective function and the detailed process is illustrated step by step. Finally, empirical studies on clustering tasks have demonstrated the superiority of FAE over the state of the art.

Clustering is a popular research topic in the field of data mining, in which the clustering method based on non-negative matrix factorization (NMF) has been widely employed. However, in the update process of NMF, there is no learning rate to guide the update as well as the update depends on the data itself, which leads to slow convergence and low clustering accuracy. To solve these problems, a generalized deep learning clustering (GDLC) algorithm based on NMF is proposed in this article. Firstly, a nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the elements in the matrix guided by the learning rate. Then, the gradient values corresponding to the element update are transformed into generalized weights and generalized biases, by inputting the elements as well as their corresponding generalized weights and generalized biases into the nonlinear activation function to construct the GDLC algorithm. In addition, for improving the understanding of the GDLC algorithm, its detailed inference procedure and algorithm design are provided. Finally, the experimental results on eight datasets show that the GDLC algorithm has efficient performance.

Co-clustering methods make use of the correlation between samples and attributes to explore the co-occurrence structure in data. These methods have played a significant role in gene expression analysis, image segmentation, and document clustering. In bipartite graph partition-based co-clustering methods, the relationship between samples and attributes is described by constructing a diagonal symmetric bipartite graph matrix, which is clustered by the philosophy of spectral clustering. However, this not only has high time complexity but also the same number of row and column clusters. In fact, the number of categories of rows and columns often changes in the real world. To address these problems, this paper proposes a novel fast flexible bipartite graph model for the co-clustering method (FBGPC) that directly uses the original matrix to construct the bipartite graph. Then, it uses the inflation operation to partition the bipartite graph in order to learn the co-occurrence structure of the original data matrix based on the inherent relationship between bipartite graph partitioning and co-clustering. Finally, hierarchical clustering is used to obtain the clustering results according to the set relationship of the co-occurrence structure. Extensive empirical results show the effectiveness of our proposed model and verify the faster performance, generality, and flexibility of our model.

Daily schedule recommendation is an intelligent approach to recommend multiple suitable activity locations and activity sequences for users based on their needs in a day. In such a scenario, training the model using traditional methods requires centralized data collection from individual users, which may be prohibited by data protection acts, such as GDPR and CCPA. In this paper, we address the problem of daily schedule recommendation utilizing the deep reinforcement learning model in a federated learning framework (FedDSR). And curriculum learning is applied to guide the training process towards better local optimization and better generalization. For the uploaded local parameters, a similarity aggregation algorithm is proposed to improve the quality of the model. The experimental results show that the proposed FedDSR model is superior and effective to multiple baselines on two real datasetsGeolifeandChengdu. Comparing with baselines, our method not only ensures that the parties do not need to share data and thus achieve joint modeling, but also can exceed$\sim\!\! 18\%$under evaluation metricperimeterand improve$\sim\! 0.72\%$under evaluation metricADTS.

Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods assume that all of views are complete. However, multi-view data are usually incomplete, i.e., a part of instances are presented on some views but not all views. Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views. In this paper, we propose a complementary and consensus learning-based incomplete multi-view unsupervised feature selection method (C$^{2}$IMUFS) to address the aforementioned issues. Concretely, C$^{2}$IMUFS integrates feature selection into an extended weighted non-negative matrix factorization model equipped with adaptive learning of view-weights and a sparse$\ell _{2,p}$-norm, which can offer better adaptability and flexibility. By the sparse linear combinations of multiple similarity matrices derived from different views, a complementary learning-guided similarity matrix reconstruction model is presented to obtain the complete similarity graph in each view. Furthermore, C$^{2}$IMUFS learns a consensus clustering indicator matrix across different views and embeds it into a spectral graph term to preserve the local geometric structure. Comprehensive experimental results on real-world datasets demonstrate the effectiveness of C$^{2}$IMUFS compared with state-of-the-art methods.

Recommender system can provide users with the required information accurately and efficiently, playing a very important role in improving users' life experience. Although knowledge graph-based recommender system can solve the sparsity and cold start problems faced by traditional recommender system, it cannot handle the cross-domain cold start problem and cannot provide multi-domain recommendations. Therefore, this paper focuses on multi-domain item-item (I2I) recommendation based on cross-domain knowledge graph embedding by analyzing the association between items of the same domain and the interaction between items of diverse domains with the aid of knowledge graph that contains rich information. Firstly, a cross-domain knowledge graph chiasmal embedding approach is proposed to efficiently interact all items in multiple domains. To help achieve both homo-domain embedding and hetero-domain embedding of items, a binding rule is put forward. Secondly, a multi-domain I2I recommendation method is presented to efficiently recommend items in multiple domains, which is a recommendation method based on link prediction of knowledge graph. Finally, the proposed methods are compared and analyzed with some benchmark methods using two datasets. The experimental results show that the proposed methods achieve better link prediction results and multi-domain recommendation results.

Urban flow analysis is an essential research for smart city construction, in which urban flow pattern analysis focuses on the continuous state of urban flow. How to mine, store and reuse traffic patterns from urban multi-source heterogeneous big data is challenging. Therefore, this paper proposes a knowledge mining network for regional flow pattern to mine and store the urban flow pattern. The proposed model consists of two modules. In the first module, the features of the region and its flow pattern are extracted as the entity and relation, respectively. In the second module, POI features are modeled to enhance the embedding representation of relation and entity. Based on the translation distance method, the knowledge triplets of regional flow patterns are mined. Finally, the proposed model is compared with some benchmark methods using Chengdu Didi order and POI datasets. Experimental results show that the proposed model is effective. In addition, the knowledge triplets are visualized and some application examples are introduced.

Feature selection refers to choose an optimal non-redundant feature subset with minimal degradation of learning performance and maximal avoidance of data overfitting. The appearance of large data explosion leads to the sequential execution of algorithms are extremely time-consuming, which necessitates the scalable parallelization of algorithms by efficiently exploiting the distributed computational capabilities. In this paper, we present parallel feature selection algorithms underpinned by a rough hypercuboid approach in order to scale for the growing data volumes. Metrics in terms of rough hypercuboid are highly suitable to parallel distributed processing, and fits well with the Apache Spark cluster computing paradigm. Two data parallelism strategies, namely, vertical partitioning and horizontal partitioning, are implemented respectively to decompose the data into concurrent iterative computing streams. Experimental results on representative datasets show that our algorithms significantly faster than its original sequential counterpart while guaranteeing the quality of the results. Furthermore, the proposed algorithms are perfectly capable of exploiting the distributed-memory clusters to accomplish the computation task that fails on a single node due to the memory constraints. Parallel scalability and extensibility analysis have confirmed that our parallelization extends well to process massive amount of data and can scales well with the increase of computational nodes.

The International Classification of Diseases (ICD) is a standard for categorizing physical conditions, which has been widely used for analyzing clinical data and monitoring health issues. Manual ICD coding takes a long time and is vulnerable to errors, so people pay more and more attention to the application of deep neural networks in ICD automatic coding. However, there is still no comprehensive review of these studies and prospects for further research. This paper is not limited to the study of deep neural networks, but gives a formal definition of ICD coding problems, and then systematically reviews the existing literature on how to design deep neural networks to address the four major challenges of ICD coding tasks. This paper also summarizes the public data sets and future research directions, to provide a guidance for the research of ICD coding in medical field.

As travel efficiency matters to the work productivity of cities, shortening passengers' travel time for metros is therefore a pressing need. To this end, we study a strategy by dynamically scheduling dwell time for trains. Developing such a strategy is challenging because of three aspects: 1) Optimizing the average travel time of passengers needs to properly balance passengers' waiting time at platforms and journey time on trains, as well as considering long-term impacts; 2) Capturing dynamic spatio-temporal (ST) correlations of incoming passengers for metro stations is difficult; and 3) For each train, the dwell time scheduling is affected by other trains, which is hard to measure. To tackle these challenges, we propose a novel deep neural network, entitled AutoDwell. Specifically, AutoDwell optimizes the long-term rewards of dwell time settings in terms of passengers' waiting and journey time by a reinforcement learning framework. Next, AutoDwell employs gated recurrent units and graph attention networks to extract the ST correlations of the passenger flows among metro stations. Moreover, attention mechanisms are leveraged in AutoDwell for capturing the interactions between the trains. Extensive experiments on two real-world datasets demonstrate the superior performance of AutoDwell over several baselines, capable of saving passengers' travel time significantly.

Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, the problem is challenging. First, different metro stations, e.g. transfer stations and non-transfer stations have unique traffic patterns. Second, it is difficult to model complex spatio-temporal dynamic relation of metro stations. To address these challenges, we develop a spatio-temporal dynamic graph relational learning model (STDGRL) to predict urban metro station flow. First, we propose a spatio-temporal node embedding representation module to capture the traffic patterns of different stations. Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix. Finally, we provide a transformer-based long-term relationship prediction module for long-term metro flow prediction. Extensive experiments are conducted based on metro data in four cities, China, with experimental results demonstrating the advantages of our method compared over 14 baselines for urban metro flow prediction.

Similarity search has recently become an integral part of many trajectory data analysis tasks. As the number of trajectories increases, we must find similar trajectories among massive trajectories, necessitating a scalable and efficient frame-work. Typically, massive trajectory data can be managed by key-value data stores. However, existing works with key-value data stores use a coarse representation to store trajectory data. Besides, they do not provide efficient query processing to search similar trajectories. Thus, this paper proposes TraSS, an efficient framework for trajectory similarity search in key-value data stores. We propose a novel spatial index, XZ*, which utilizes fine-grained index spaces with irregular shapes and sizes to represent trajectories elaborately. Further, we devise a bijective function from the index spaces of XZ* to continuous integers, which is simple but effective for query processing. To improve the efficiency of similarity search, we employ two steps to prune dissimilar trajectories: (1) global pruning. It leverages the XZ* index to prune index spaces with no trajectories similar to the query trajectory. Our global pruning can only pick out index spaces with similar sizes and shapes to the query trajectory. Compared to the state-of-the-art index, our global pruning reduces I/O overhead up to 66.4 % during query processing; (2) local filtering. It filters dissimilar trajectories in a way with low complexity. We use a few representative features extracted from a trajectory by the Douglas-Peucker algorithm to accelerate the local filtering. We implement an open-source toolkit (TraSS) on a popular key-value data store. Extensive experiments show that TraSS outperforms state-of-the-art solutions.

In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we apply three clusterers (K-means, affinity propagation and spectral clustering algorithms) to obtain three different clustering partitions (CPs) without any background knowledge or label. Then, an unanimous voting strategy is used to generate a local clustering partition (LCP). The novel MIRBM model is a core feature encoding part of the proposed unsupervised feature learning architecture. The novelty of it is that the LCP as an unsupervised guidance is integrated into one step contrastive divergence (${\mathtt{{CD}}}_{1}$) learning to guide the distribution of the hidden layer features. For the instance in the same LCP cluster, the hidden and reconstructed hidden layer features of the MIRBM model in the proposed architecture tend to constrict together in the training process. Meanwhile, each LCP center tends to disperse from each other as much as possible in the hidden and reconstructed hidden layer during training. The experiments demonstrate that the proposed unsupervised feature learning architecture has more powerful feature representation and generalization capability than the state-of-the-art models for clustering tasks in the Microsoft Research Asia Multimedia (MSRA-MM)2.0 dataset.

The natural gas tightly correlates with our everyday life. However, driven by gray incomes, some users are prone to stealing gas by refitting the equipment without permission. Especially for the boiler room users in winter, this phenomenon appears more rampant. Traditional gas-theft detection methods highly rely on the on-site inspection, where exists ineffective and randomness. With the rapidly deployed IoT sensors, we can collect real-time gas consumption data to analyze users’ behavior patterns, where the gas-theft suspects could be discovered early and accurately. In this paper, we propose a data-driven approach, named SVOC, to detect gas-theft suspects among boiler room users. Our approach consists of a scenario-based data quality detection algorithm, a deformation-based normality detection algorithm, and an One-Class Support Vector Machine (OCSVM) based anomaly detection algorithm. Specifically, considering the temporal proximity between the gas consumption and the outdoor temperature, the normality detection algorithm adopts a similarity-based deformation correlation to detect normal boiler room users out of abnormal ones. Then, we employ OCSVM as the anomaly detection algorithm to capture various features across multiple data sources, aiming to distinguish gas-theft suspects from the remaining irregular users. Here, the detected normal and abnormal users are fed into the OCSVM for training and prediction, respectively, which can overcome the label scarcity problem. We conduct extensive experiments on a real-world dataset during one heating season. The results demonstrate distinct advantages of our approach over various baselines. We have developed a real-time system on the cloud, providing daily gas-theft suspects for gas companies.

With the rapid development of the Internet of Things (IoT), massive trajectories have been generated. Trajectory data is beneficial for many urban applications. This demo presents a holistic trajectory data management system based on distributed platforms, such as Spark and HBase, namely JUST-Traj. It provides a variety of indexes to efficiently support spatio-temporal queries and analyses on massive trajectories. Additionally, it provides a convenient SQL engine to execute all operations (storage, queries, analyses) through a SQL-like statement. Finally, we design a web portal for developers and demonstrate different operations in the portal.

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this article, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture. Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (1D-CNNs) and Bi-directional Long Short-term Memory networks (Bi-LSTM). The former is to extract the local trend features and spatial correlation features, and the latter is to learn spatial-temporal dependencies. Then we design a jointly hybrid deep learning framework based on one-dimensional CNNs and Bi-LSTM for shared representation features learning of multivariate air quality related time series data. We conduct extensive experimental evaluations using two real-world datasets, and the results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy.

Gas theft of restaurants is a major concern in the gas industry, which causes revenue losses for gas companies and endangers the public safety seriously. Traditional methods of gas theft detection highly rely on active human efforts that are extremely ineffective. Thanks to the gas consumption data collected by smart meters, we can devise a data-driven method to tackle this issue. In this paper, we propose a gas-theft detection method msRank to discover suspicious restaurant users when only scarce labels are available. Our method contains three main components: 1)data pre-processing, which filters reading noises and excludes data-missing or zero-use users; 2)normal user modeling, which quantifies the self-stable seasonality of normal users and distinguishes them from unstable ones; and 3)gas-theft suspect detection, which discovers gas-theft suspects among unstable users by RankNet-based suspicion scoring on extracted deviation features. By using detected normal users as negative samples to train RankNet, the component of normal user modeling and that of gas-theft suspect detection are seamlessly connected, overcoming the problem of label scarcity. We conduct extensive experiments on three real-world datasets, and the results demonstrate advantages of our approach. We have deployed a system GasShield which provides a gas-theft suspect list weekly for a gas group in northern China.

Emergency Medical Services (EMS) are of great importance to saving people's lives from emergent accidents and diseases by efficiently picking up patients using ambulances. The transporting capability of an EMS system (e.g., defined as the average pickup time of patients) significantly depends on the real-time redeployment strategy of ambulances. That is, which station should an ambulance be redeployed to, after it becomes available (after it transports a patient to a hospital or after it finishes the in-site treatment for a patient)? However, it is a challenging task concerning with the multiple data D1-D5 as detailed in Introduction. To this end, in this paper, we propose a data-driven real-time ambulance redeployment approach that redeploys an ambulance to a proper station after it becomes available, so as to optimize the transporting capability of an EMS system, considering the aforementioned multiple data D1-D5. Specifically, the proposed approach is comprised of two stages to well consider the D1-D5. First, we propose a method (a safety time-based urgency index) to incorporate D1, D2, and D3 into each ambulance station's urgency degree (D*). Second, we propose an optimal matching algorithm to combine D*, D4, and D5 into the redeployment of the current available ambulance. Experimental results using data collected in real world demonstrate the significant advantages of our approach over many baselines. Comparing with baselines, our approach can save ~4 minutes (~35 percent) of the average pickup time for each patient, improve the ratio of patients picked up within 10 minutes from 0.684 and 0.803 (~17 percent), and largely enhance the survival rate of patients (~12 percent for patients in category A1 and ~17 percent for patients in A2).

With the increasing vehicles on the road, it is becoming more and more important to sense citywide traffic, which is of great benefit to the government's policy-making and people's decision making. Currently, traffic speed and volume information are mostly derived from GPS trajectories data and volume sensor records respectively. Unfortunately, speed and volume information suffer from serious data missing problem. Speed can be absent at arbitrary road segment and time slot, while volume is only recorded by limited volume sensors. For modeling citywide traffic, inspired by the observations of missing patterns and prior knowledge about traffic, we propose a neural memorization and generalization approach to infer the missing speed and volume, which mainly consists of a memorization module for speed inference and a generalization module for volume inference. Considering the temporal closeness and period properties, memorization module takes advantage of neural multi-head self-attention architecture to memorize the intrinsic correlations from historical traffic information. Generalization module adopts neural key-value attention architecture to generalize the extrinsic dependencies among volume sensors by exploiting road contexts. We conduct extensive experiments on two real-world datasets in two cities, Guiyang and Jinan, and the experimental results consistently demonstrate the advantages of our approach. We have developed a real-time system on the cloud, entitled CityTraffic, providing citywide traffic speed and volume information and fine-grained pollutant emission of vehicles in Guiyang city.

Weather forecasting is usually solved through numerical weather prediction (NWP), which can sometimes lead to unsatisfactory performance due to inappropriate setting of the initial states. In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data that incorporates prior knowledge from NWP. We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function. A notable advantage of our proposed method is that it simultaneously implements single-value forecasting and uncertainty quantification, which we refer to as deep uncertainty quantification (DUQ). Efficient deep ensemble strategies are also explored to further improve performance. This new approach was evaluated on a public dataset collected from weather stations in Beijing, China. Experimental results demonstrate that the proposed NLE loss significantly improves generalization compared to mean squared error (MSE) loss and mean absolute error (MAE) loss. Compared with NWP, this approach significantly improves accuracy by 47.76%, which is a state-of-the-art result on this benchmark dataset.

Ordering take-out food (a.k.a. takeaway food) on online-to-offline (O2O) food ordering and delivery platforms is becoming a new lifestyle for people living in big cities, thanks to its great convenience. Web users and mobile device users can order take-out food (i.e. obtain online food ordering services) on an O2O platform. Then the O2O platform will dispatch food carriers to deliver food from restaurants to users, i.e. providing users with offline food delivery services. For an O2O food ordering and delivery platform, improving food delivery efficiency, given the massive number of food orders each day and the limited number of food carriers, is of paramount importance to reducing the length of time users wait for their food. Thus, in this paper, we study the food delivery task grouping problem so as to improve food delivery efficiency and alleviate the pain of waiting for users, which to the best of our knowledge has not been studied yet. However, the food delivery task grouping problem is challenging, given two reasons. First, the food delivery efficiency is affected by multiple factors, which are non-trivial to formulate and jointly consider. Second, the problem is a typical NP-hard problem and to find near-optimal grouping results is not easy. To address these two issues, we propose an effective task grouping method. On one hand, we provide formal formulations for the factors affecting the food delivery efficiency, and provide an objective to organically combine these factors such that it can better guide the task grouping. On the other hand, we propose heuristic algorithms to efficiently obtain effective task grouping results, consisting of a greedy algorithm and a replacement algorithm. We evaluate our task grouping method using take-out food order data from web users and mobile device users on a real-world O2O food ordering and delivery platform. Experiment results demonstrate that our task grouping method can save ~ 16% (87 seconds) of average waiting time for each user, comparing with many baseline methods. It indicates that our method is able to significantly improve the food delivery efficiency and can provide better food delivery services for users.

This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: (1) The reconstruction error of the input cannot be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also cannot be lower than a lower bound. (2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. (3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termedDouble Denoising Auto-Encoders(DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pretraining methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models.

Accompanying the rapid urbanization, many developing countries are suffering from serious air pollution problem. The demand for predicting future air quality is becoming increasingly more important to government's policy-making and people's decision making. In this paper, we predict the air quality of next 48 hours for each monitoring station, considering air quality data, meteorology data, and weather forecast data. Based on the domain knowledge about air pollution, we propose a deep neural network (DNN)-based approach (entitled DeepAir), which consists of a spatial transformation component and a deep distributed fusion network. Considering air pollutants' spatial correlations, the former component converts the spatial sparse air quality data into a consistent input to simulate the pollutant sources. The latter network adopts a neural distributed architecture to fuse heterogeneous urban data for simultaneously capturing the factors affecting air quality, e.g. meteorological conditions. We deployed DeepAir in our AirPollutionPrediction system, providing fine-grained air quality forecasts for 300+ Chinese cities every hour. The experimental results on the data from three-year nine Chinese-city demonstrate the advantages of DeepAir beyond 10 baseline methods. Comparing with the previous online approach in AirPollutionPrediction system, we have 2.4%, 12.2%, 63.2% relative accuracy improvements on short-term, long-term and sudden changes prediction, respectively.

Attribute reduction based on rough set theory has attracted much attention recently. In real-life applications, many decision tables may vary dynamically with time, e.g., the variation of attributes, objects, and attribute values. The reduction of decision tables may change on the alteration of attribute values. The paper focuses on dynamic maintenance of attribute reduction when varying data values of multiple objects. Incremental mechanisms for knowledge granularity are proposed first, which aims to update attribute reduction effectively. Then, a group incremental reduction algorithm with varying data values is developed. When attribute values of multiple objects have been replaced by new ones in decision table, the proposed incremental algorithm can find the new reduct in a much shorter time. The time complexity analysis and experiments on different data sets from UCI have validated that the proposed incremental algorithms are efficient and effective to update the reduction with the variation of attribute values.

Real-world datasets often have representations in multiple views or come from multiple sources. Exploiting consistent or complementary information from multi-view data, multi-view clustering aims to get better clustering quality rather than relying on the individual view. In this paper, we propose a novel multi-view clustering method called multi-view concept clustering based on concept factorization with local manifold regularization, which drives a common consensus representation for multiple views. The local manifold regularization is incorporated into concept factorization to preserve the locally geometrical structure of the data space. Moreover, the weight of each view is learnt automatically and a co-normalized approach is designed to make fusion meaningful in terms of driving the common consensus representation. An iterative optimization algorithm based on the multiplicative rules is developed to minimize the objective function. Experimental results on nine reality datasets involving different fields demonstrate that the proposed method performs better than several state-of-the-art multi-view clustering methods.

In this paper, we forecast the reading of an air quality monitoring station over the next 48 hours, using a data-driven method that considers current meteorological data, weather forecasts, and air quality data of the station and that of other stations within a few hundred kilometers. Our predictive model is comprised of four major components: 1) a linear regression-based temporal predictor to model the local factors of air quality, 2) a neural network-based spatial predictor to model global factors, 3) a dynamic aggregator combining the predictions of the spatial and temporal predictors according to meteorological data, and 4) an inflection predictor to capture sudden changes in air quality. We evaluate our model with data from 43 cities in China, surpassing the results of multiple baseline methods. We have deployed a system with the Chinese Ministry of Environmental Protection, providing 48-hour fine-grained air quality forecasts for four major Chinese cities every hour. The forecast function is also enabled on Microsoft Bing Map and MS cloud platform Azure. Our technology is general and can be applied globally for other cities.

The rough set model over dual universes (RSMDU) as a generalized model of classical rough set theory (RST) on the two universes has been well studied with the objective to establishment of model and discussion of its corresponding properties. Approximations of a concept in RSMDU, which may further be applied to knowledge discovery or related work, need to be updated effectively under a dynamic environment. Despite recent advances in using the incremental method to speed up updating approximations of RST, there has been little effort toward incorporating the incremental method into computing approximations under RSMDU. This paper proposes an incremental learning approach for updating approximations in RSMDU when the objects of two universes vary with time. An illustration is employed to show the proposed method. Extensive experimental results on various real and synthetic data sets verify the effectiveness of the proposed incremental updating method while comparing with the nonincremental method.

With the extraordinary spread of computers and sensors, enormous amounts of data are generated every day in a range of areas-search engines, social media, healthcare organizations, insurance companies, financial industry, retail, and many others.Data science refers to the theories, methods, and applications for extracting previously unavailable and potentially highly useful information from data.This field has evolved as a hybrid of research in data mining, machine learning, computational intelligence, databases, algorithms, statistics, operations research, visualization, privacy, and security.It is helping us make sense out of vast quantities of information.However, how to use these data by an effective and ethical way is a significant challenge to science and to society as a whole.Intelligent techniques, including artificial intelligence, neural networks, fuzzy logic, granular computing, rough sets, expert systems, case-based reasoning, evolutionary algorithms, and swarm computing, have been successfully applied in many fields including data science.This special issue is devoted to the use of intelligent techniques for data science that reflects their current development obtained from selected papers submitted to the 8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE2013) held in Shenzhen, People's Republic of China, during November 20-23, 2013.This issue encompasses seven papers that present the application of different intelligent techniques to different data science problems ranging from recommender systems to recognition processes passing by others such as activity simulation, fuzzy trading systems, deep learning, and incremental learning.The paper coauthored by Wei Wang, Guangquan Zhang, and Jie Lu investigates the collaborative filtering with an entropy-driven user similarity in recommender systems.It aims at improving recommendation performance, by means of a novel

As the volume of data grows at an unprecedented rate, large-scale data mining and knowledge discovery present a tremendous challenge. Rough set theory, which has been used successfully in solving problems in pattern recognition, machine learning, and data mining, centers around the idea that a set of distinct objects may be approximated via a lower and upper bound. In order to obtain the benefits that rough sets can provide for data mining and related tasks, efficient computation of these approximations is vital. The recently introduced cloud computing model, MapReduce, has gained a lot of attention from the scientific community for its applicability to large-scale data analysis. In previous research, we proposed a MapReduce-based method for computing approximations in parallel, which can efficiently process complete data but fails in the case of missing (incomplete) data. To address this shortcoming, three different parallel matrix-based methods are introduced to process large-scale, incomplete data. All of them are built on MapReduce and implemented on Twister that is a lightweight MapReduce runtime system. The proposed parallel methods are then experimentally shown to be efficient for processing large-scale data.

Rule induction method based on rough set theory (RST) has received much attention recently since it may generate a minimal set of rules from the decision system for real-life applications by using of attribute reduction and approximations. The decision system may vary with time, e.g., the variation of objects, attributes and attribute values. The reduction and approximations of the decision system may alter on Attribute Values' Coarsening and Refining (AVCR), a kind of variation of attribute values, which results in the alteration of decision rules simultaneously. This paper aims for dynamic maintenance of decision rules w.r.t. AVCR. The definition of minimal discernibility attribute set is proposed firstly, which aims to improve the efficiency of attribute reduction in RST. Then, principles of updating decision rules in case of AVCR are discussed. Furthermore, the rough set-based methods for updating decision rules in the inconsistent decision system are proposed. The complexity analysis and extensive experiments on UCI data sets have verified the effectiveness and efficiency of the proposed methods.

Dominance-based rough sets approach (DRSA) is an effective tool to deal with information with preference-ordered attribute domains and decision classes. Any information system may evolve when new objects enter into or old objects get out. Approximations of DRSA need update for decision analysis or other relative tasks. Incremental updating is a feasible and effective technique to update approximations. The purpose of this paper is to present an incremental approach for updating approximations of DRSA. The approach is applicable to dynamic information systems when the set of objects varies over time. In this paper, we discuss the principles of incrementally updating P-dominating sets and P-dominated sets and propose an incremental approach for updating approximations of DRSA. A numerical example is given to illustrate the incremental approach. The experimental evaluations on data sets from UCI show that the incremental approach outperforms the original nonincremental one.

Approximations of a concept by a variable precision rough-set model (VPRS) usually vary under a dynamic information system environment. It is thus effective to carry out incremental updating approximations by utilizing previous data structures. This paper focuses on a new incremental method for updating approximations of VPRS while objects in the information system dynamically alter. It discusses properties of information granulation and approximations under the dynamic environment while objects in the universe evolve over time. The variation of an attribute's domain is also considered to perform incremental updating for approximations under VPRS. Finally, an extensive experimental evaluation validates the efficiency of the proposed method for dynamic maintenance of VPRS approximations.

Approximations of a concept in rough set theory induce rules and need to update for dynamic data mining and related tasks. Most existing incremental methods based on the classical rough set model can only be used to deal with the categorical data. This paper presents a new dynamic method for incrementally updating approximations of a concept under neighborhood rough sets to deal with numerical data. A comparison of the proposed incremental method with a nonincremental method of dynamic maintenance of rough set approximations is conducted by an extensive experimental evaluation on different data sets from UCI. Experimental results show that the proposed method effectively updates approximations of a concept in practice. © 2012 Wiley Periodicals, Inc.

In rough set theory, upper and lower approximations for a concept will change dynamically as the information system changes over time. How to update approximations based on the original information is an important task that can help improve the efficiency of knowledge discovery. This paper focuses on the approach of dynamically updating approximations when attribute values are coarsened or refined. The main contributions include: (1) defining coarsening and refining attribute values in information systems and introducing the properties and the principles of coarsening and refining attribute values; (2) analyzing the properties for dynamic maintenance in terms of upper and lower approximations with coarsening and refining attribute values; (3) proposing an incremental algorithm for updating the approximations of a concept as coarsening or refining attributes values; and finally (4) validating the efficiency of the proposed approach to handle the dynamic maintenance of the approximations for a given concept. © 2010 Wiley Periodicals, Inc.