Combining Mixture of Experts (MoE) with Low-Rank Adaptation (LoRA) has shown promising efficiency in multi-task instruction tuning for Large Language Models (LLMs). While existing routing schemes for such MoE systems employ auxiliary functions to ensure both expert selection certainty and workload balance among experts, they are hindered by two critical challenges: (1) Existing methods overlook the evolving cross-expert relationships across layers, leading to inefficient expert utilization. (2) The auxiliary functions fail to incorporate cross-task semantic characteristics during expert assignment, leading to suboptimal task adaptation. To address these challenges, we propose Hybrid routing for a Mixture of LoRA Experts (HotMoE), a novel multi-task instruction tuning framework that adapts hierarchical routing to the distinct characteristics of different LLM layers. First, we design a hybrid routing module. In lower layers, expert-expert attention facilitates cross-task collaboration and generalization. In higher layers, token-expert attention enables precise alignment between task semantics and specialized experts. Second, we introduce a similarity-guided auxiliary loss module to regularize routing decisions by exploiting hidden state similarities. This loss synergistically reinforces expert specialization without sacrificing certainty of expert selection by promoting cohesive activation patterns among semantically related tasks while sharpening distinctions between conflicting ones. Experiments across two multi-task instruction tuning scenarios covering seven NLP benchmarks demonstrate that HotMoE consistently outperforms all baselines, improving Mean Relative Difference by up to 1.68% with only 3.1% of trainable parameters.

The reputation of universities has drawn increasing attention in recent years, especially with the emergence of various rankings. However, despite advances in big data technologies that facilitate data collection and analysis, accurately defining and balancing factors related to university reputation and educational quality remains complex and tedious. Moreover, current educational assessment methods exhibit notable differences and controversies. In this paper, we present Iva , a human-in-the-loop I ntelligent V isual A ssessment system for higher education quality. This system utilizes large language models to analyze extensive multi-modal educational data, with visualization techniques incorporated to enable multi-scale exploration and interaction. Our extensive evaluations, including a carefully-designed user study and expert interviews, demonstrate the system’s potential value and provide insights for future improvements.

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after deployment. Training APR on a fixed dataset leads to overfitting, making it fail catastrophically on challenging novel data. This work proposes Continual Domain Expansion (ConDo), which continually collects unlabeled inference data to update the deployed APR. Instead of applying standard unsupervised domain adaptation methods which are ineffective for APR, ConDo effectively learns from unlabeled data by distilling knowledge from scene-agnostic localization methods. By sampling data uniformly from historical and newly collected data, ConDo can effectively expand the generalization domain of APR. Large-scale benchmarks with various scene types are constructed to evaluate models under practical (long-term) data changes. ConDo consistently and significantly outperforms baselines across architectures, scene types, and data changes. On challenging scenes (Fig.1), it reduces the localization error by >7x (14.8m vs 1.7m). Analysis shows the robustness of ConDo against compute budgets, replay buffer sizes and teacher prediction noise. Comparing to model re-training, ConDo achieves similar performance up to 25x faster.

Federated learning (FL) is emerging as a promising technique for collaborative learning without local data leaving their devices. However, clients’ data originating from diverse domains may degrade model performance due to domain shifts, preventing the model from learning consistent representation space. In this paper, we propose a novel FL framework, Federated Domain Shift Eraser (FDSE), to improve model performance by differently erasing each client’s domain skew and enhancing their consensus. First, we formulate the model forward passing as an iterative deskewing process that extracts and then deskews features alternatively. This is efficiently achieved by decomposing each original layer in the neural network into a Domain-agnostic Feature Extractor (DFE) and a Domain-specific Skew Eraser (DSE). Then, a regularization term is applied to promise the effectiveness of feature deskewing by pulling local statistics of DSE’s outputs close to the globally consistent ones. Finally, DFE modules are fairly aggregated and broadcast to all the clients to maximize their consensus, and DSE modules are personalized for each client via similarity-aware aggregation to erase their domain skew differently. Comprehensive experiments were conducted on three datasets to confirm the advantages of our method in terms of accuracy, efficiency, and generalizability.

In recent years, graph neural networks (GNNs) have been commonly utilized for social recommendation systems. However, real-world scenarios often present challenges related to user privacy and business constraints, inhibiting direct access to valuable social information from other platforms. While many existing methods have tackled matrix factorization-based social recommendations without direct social data access, developing GNN-based federated social recommendation models under similar conditions remains largely unexplored. To address this issue, we propose a novel vertical federated social recommendation method leveraging privacy-preserving two-party graph convolution networks (P4GCN) to enhance recommendation accuracy without requiring direct access to sensitive social information. First, we introduce a Sandwich-Encryption module to ensure comprehensive data privacy during the collaborative computing process. Second, we provide a thorough theoretical analysis of the privacy guarantees, considering the participation of both curious and honest parties. Extensive experiments on four real-world datasets demonstrate that P4GCN outperforms state-of-the-art methods in terms of recommendation accuracy.

Wheel-legged robots integrate the adaptability of legged locomotion with the efficiency of wheeled movement, enabling agile traversal across diverse terrains. However, abrupt terrain transitions introduce substantial state variations, including velocity fluctuations, posture shifts, and slippage, which pose significant challenges to locomotion stability. To address these issues, we propose a state error compensation framework that integrates a residual network with a trust-region mechanism. The residual network implicitly captures nonlinear contact dynamics, enabling real-time correction of slippage-induced state deviations, while the trust-region mechanism regulates compensation amplitude to maintain stable locomotion. Furthermore, we introduce a dual-source contrastive learning strategy, which explicitly differentiates terrain-induced transitions from external perturbations, facilitating context-aware error recovery. The proposed framework is integrated into a model-free reinforcement learning pipeline, ensuring adaptability to previously unseen environments. To further enhance robustness, an uncertainty-aware calibration module is introduced. This module dynamically adjusts the trust region boundary in real time, leveraging sensory feedback to adaptively constrain residual corrections and prevent over-adjustment, thereby maintaining stability during diverse terrain transitions. Experimental results demonstrate that the proposed framework achieves a 96.7% terrain traversal success rate and 92% velocity tracking accuracy under dynamic disturbances. On unstructured and mixed terrains, it maintains a mean velocity tracking error of 0.15 m/s and stable posture, with pitch and roll angles constrained to ±0.04 rad and ±0.02 rad, respectively.

LiDAR-based 3D object detection models inevitably struggle under rainy conditions due to the degraded and noisy scanning signals. Previous research has attempted to address this by simulating the noise from rain to improve the robustness of detection models. However, significant disparities exist between simulated and actual rain-impacted data points. In this work, we propose a novel rain simulation method, termed DRET, that unifies Dynamics and Rainy Environment Theory to provide a cost-effective means of expanding the available realistic rain data for 3D detection training. Furthermore, we present a Sunny-to-Rainy Knowledge Distillation (SRKD) approach to enhance 3D detection under rainy conditions. Extensive experiments on the Waymo-Open-Dataset show that, when combined with the state-of-the-art DSVT model and other classical 3D detectors, our proposed framework demonstrates significant detection accuracy improvements, without losing efficiency. Remarkably, our framework also improves detection capabilities under sunny conditions, therefore offering a robust solution for 3D detection regardless of whether the weather is rainy or sunny.

An increasing number of related urban data sources have brought forth novel opportunities for learning urban region representations, i.e., embeddings. The embeddings describe latent features of urban regions and enable discovering similar regions for urban planning applications. Existing methods learn an embedding for a region using every different type of region feature data, and subsequently fuse all learned embeddings of a region to generate a unified region embedding. However, these studies often overlook the significance of the fusion process. The typical fusion methods rely on simple aggregation, such as summation and concatenation, thereby disregarding correlations within the fused region embeddings. To address this limitation, we propose a novel model named HAFusion. Our model is powered by a dual-feature attentive fusion module named DAFusion, which fuses embeddings from different region features to learn higher-order correlations be-tween the regions as well as between the different types of region features. DAFusion is generic - it can be integrated into existing models to enhance their fusion process. Further, motivated by the effective fusion capability of an attentive module, we propose a hybrid attentive feature learning module named HALearning to enhance the embedding learning from each individual type of region features. Extensive experiments on three real-world datasets demonstrate that our model HAFusion outperforms state-of-the-art models across three different prediction tasks. Using our learned region embeddings leads to consistent and up to 31 % improvements in the prediction accuracy.

Collaborative fairness stands as an essential element in federated learning to encourage client participation by equitably distributing rewards based on individual contributions. Existing methods primarily focus on adjusting gradient allocations among clients to achieve collaborative fairness. However, they frequently overlook crucial factors such as maintaining consistency across local models and catering to the diverse requirements of high-contributing clients. This oversight inevitably decreases both fairness and model accuracy in practice. To address these issues, we propose FedSAC, a novel Federated learning framework with dynamic Submodel Allocation for Collaborative fairness, backed by a theoretical convergence guarantee. First, we present the concept of "bounded collaborative fairness (BCF)", which ensures fairness by tailoring rewards to individual clients based on their contributions. Second, to implement the BCF, we design a submodel allocation module with a theoretical guarantee of fairness. This module incentivizes high-contributing clients with high-performance submodels containing a diverse range of crucial neurons, thereby preserving consistency across local models. Third, we further develop a dynamic aggregation module to adaptively aggregate submodels, ensuring the equitable treatment of low-frequency neurons and consequently enhancing overall model accuracy. Extensive experiments conducted on three public benchmarks demonstrate that FedSAC outperforms all baseline methods in both fairness and model accuracy. We see this work as a significant step towards incentivizing broader client participation in federated learning. The source code is available at https://github.com/wangzihuixmu/FedSAC.

The advancement of federated learning (FL) has enabled model training in spatio-temporal graphs (STG) while addressing privacy concerns that restrict direct data sharing. However, capturing spatio-temporal dependencies in FL settings that involve subgraphs remains challenging. On one hand, nodes in a subgraph struggle to model spatio-temporal correlations across different subgraphs. On the other hand, temporal data (e.g., traffic flows) often exhibits distinct non-i.i.d. patterns across subgraphs. To address these challenges, we propose a novel framework called Federated Subgraph Learning over Spatio-Temporal Graphs (FedSTG) for effective traffic prediction. FedSTG leverages attention-based models and includes two key modules. First, the Isolated Nodes Connection module integrates and filters the cluster centers of node embeddings from other subgraphs to enhance local model training, thereby capturing spatio-temporal correlations between nodes across subgraphs. Second, the Personalized Model Aggregation module compute the similarities between clients pairwise using their local models and aggregates personalized models based on the similarities as aggregation weights, thereby ensuring the effective model aggregation. Extensive experiments on two attention-based models and three commonly used traffic prediction datasets demonstrate the superiority of FedSTG.

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods.

Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overlooked the dynamic spatio-temporal correlations among nodes by using the same adjacency matrix across different time steps. To address these limitations, we propose a novel approach called Spatio-Temporal Joint Graph Convolutional Networks (STJGCN) for accurate traffic forecasting on road networks over multiple future time steps. Specifically, our method encompasses the construction of both pre-defined and adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which represent comprehensive and dynamic spatio-temporal correlations. We further introduce dilated causal spatio-temporal joint graph convolution layers on the STJG to capture spatio-temporal dependencies from distinct perspectives with multiple ranges. To aggregate information from different ranges, we propose a multi-range attention mechanism. Finally, we evaluate our approach on five public traffic datasets and experimental results demonstrate that STJGCN is not only computationally efficient but also outperforms 11 state-of-the-art baseline methods.

While federated learning has shown strong results in opti- mizing a machine learning model without direct access to the original data, its performance may be hindered by in- termittent client availability which slows down the conver- gence and biases the final learned model. There are significant challenges to achieve both stable and bias-free training un- der arbitrary client availability. To address these challenges, we propose a framework named Federated Graph-based Sam- pling (FEDGS), to stabilize the global model update and mitigate the long-term bias given arbitrary client availabil- ity simultaneously. First, we model the data correlations of clients with a Data-Distribution-Dependency Graph (3DG) that helps keep the sampled clients data apart from each other, which is theoretically shown to improve the approximation to the optimal model update. Second, constrained by the far- distance in data distribution of the sampled clients, we fur- ther minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias. To validate the effectiveness of FEDGS, we conduct experiments on three datasets under a comprehensive set of seven client availability modes. Our experimental results confirm FEDGS’s advantage in both enabling a fair client-sampling scheme and improving the model performance under arbitrary client availability. Our code is available at https://github.com/WwZzz/FedGS.

Federated learning is a new distributed privacy-preserving learning paradigm which perfectly meets the requirements of many large service systems such as banking, healthcare, and smart city. Meanwhile, person re-identification, as a technology to associate the images of the same person from different data sources, has been widely used in many smart services such as smart logistics, smart surveillance, and many public searching and rescue missions. Therefore, it is a promising solution to use federated learning for person re-identification to improve the model accuracy while protecting the data privacy. However, the common problem of non-independent and identically distributed (Non-IID) data with heterogeneous clients in federated learning often causes undesirable model accuracy. To address such a problem, in this paper, we propose a novel strategy named federated learning with data augmentation for person re-identification (Fed4ReID). Specifically, to alleviate the impact of Non-IID data, we utilise a pre-trained DCGAN (Deep Convolutional Generative Adversarial Network) model for data augmentation at each edge servers. Experiments on public datasets show that our proposed strategy can outperform baseline method in general accuracy.

Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires inductive learning. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://github.com/zhengchuanpan/INCREASE.

Traffic violations have become one of the major threats to urban transportation systems, undermining human safety and causing economic losses. To alleviate this problem, crowd-based patrol forces including traffic police and voluntary participants have been employed in many cities. To adaptively optimize patrol routes with limited manpower, it is essential to be aware of traffic violation hotspots. Traditionally, traffic violation hotspots are directly inferred from experiences, and existing patrol routes are usually fixed. In this paper, we propose a mobile crowdsensing-based framework to dynamically infer traffic violation hotspots and adaptively schedule crowd patrol routes. Specifically, we first extract traffic violation-prone locations from heterogeneous crowd-sensed data and propose a spatiotemporal context-aware self-adaptive learning model (CSTA) to infer traffic violation hotspots. Then, we propose a tensor-based integer linear problem modeling method (TILP) to adaptively find optimal patrol routes under human labor constraints. Experiments on real-world data from two Chinese cities (Xiamen and Chengdu) show that our approach accurately infers traffic violation hotspots with F1-scores above 90% in both cities, and generates patrol routes with relative coverage ratios above 85%, significantly outperforming baseline methods.

Road markings extraction (RME) from 3D point clouds acquired by mobile LiDAR systems has been widely used for road safety and autonomous driving. However, due to the increasing awareness of personal data protection and national informat ion security regulations, most autonomous driving companies are not willing to share their private point clouds data with the community. Therefore, such restriction of centralized training might inevitably inhibit the effectiveness of RME procedure. Federated learning (FL) is a distributed machine learning architecture that could address the aforementioned privacy-accuracy dilemma to collaboratively learn a global RME model from multiple clients without sharing raw data. In this paper, we propose a novel FedRME, a federated road markings extraction system to collaboratively learn a global RME model with multiple privacy-preserved local models from 3D mobile LiDAR point clouds. FedRME adopt the classical FedAvg model to construct a generalizable global feature embedding model without accessing local data. Moreover, to tackle data heterogeneity problem that local models vary in point clouds volumes and categories, we design a dynamic weighting mechanism to optimize the cooperative training effectiveness before server aggregation. Experimental results on three real-world mobil e LiDAR point clouds datasets with federated learning settings demonstrate that FedRME not only achieves superior performance but also reduces computation by up to 25%.The source code is available at https://github.com/WwZzz/easyFL#FedRME.

Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35% improvement. https://github.com/wushangbin/MGFN

Traffic violations have become one of the major threats to urban transportation systems, undermining road safety and causing economic losses. Although various methods have been proposed by road authorities and researchers to find out the possible causes of traffic violations, existing methods often fail to diagnose traffic violations from drivers’ perspectives and contexts or consider their visual and comprehension loads while driving. In this work, we propose a driver-centered simulation platform to inspect drivers’ loads in traffic violation hotspots. Specifically, we first build a driving simulator based on the 3D point clouds of real-world traffic violation hotspots. We then recruit drivers to simulate driving in designated traffic scenes. Indicators for drivers’ visual and comprehension loads are derived based on drivers’ feedback. Upon this basis, we build an explainable model to automatically indicate drivers’ visual and comprehension loads under various crowd-sensed traffic scenes. Experiments using real-world data from a Chinese City (Xiamen) and case studies show that our approach successfully derives a set of prominent indicators to effectively diagnose drivers’ visual and comprehension loads in real-world traffic violation hotspots.

Nowadays, a large number of sensors are equipped on mobile or stationary platforms, which continuously generate geo-tagged and time-stamped readings (i.e., geo-sensory data) that contain rich information about the surrounding environment. These data have irregular space and time coordinates. To represent geo-sensory data, there have been extensive research efforts using time sequences, grid-like images, and graph signals. However, there still lacks a proper representation that can describe both the mobile and stationary geo-sensory data without the information-losing discretization in spatial and temporal dimensions. In this paper, we propose to represent massive geo-sensory data asspatio-temporal point clouds(STPC), and presentSTPC-Net, a novel deep neural network for processing STPC. STPC leverages the original irregular space-time coordinates, and STPC-Net captures intra-sensor and inter-sensor correlations from STPC. In this way, STPC-Net learns the key information of STPC, and overcomes challenges in data irregularity. Experiments using real-world datasets show that STPC-Net achieves state-of-the-art performance in different tasks on both mobile and stationary geo-sensory data. The source code is available athttps://github.com/zhengchuanpan/STPC-Net.

The coronavirus disease 2019 (COVID-19) break-out in late December 2019 has spread rapidly worldwide. Existing studies have shown that there is a significant correlation between large-scale human movements and the spread of the epidemic. However, there is a lack of quantification of these correlations, and it is still challenging to predict the spread of the epidemic at early stage. In this paper, we address this issue by conducting a statistical analysis on the spatio-temporal relationship between human mobility and the epidemic spread. Specifically, we proposed an improved SEIR model to adapt to the COVID-19 epidemic, so that we can predict the spread of the epidemic at the early stage using human mobility data and the early confirmed cases. We evaluated our model in various provinces and cities in China, and the results are superior to various baselines, verifying the effectiveness of the method.

Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the magnitudes. To address this issue, we propose the federated fair averaging (FedFV) algorithm to mitigate potential conflicts among clients before averaging their gradients. We first use the cosine similarity to detect gradient conflicts, and then iteratively eliminate such conflicts by modifying both the direction and the magnitude of the gradients. We further show the theoretical foundation of FedFV to mitigate the issue conflicting gradients and converge to Pareto stationary solutions. Extensive experiments on a suite of federated datasets confirm that FedFV compares favorably against state-of-the-art methods in terms of fairness, accuracy and efficiency. The source code is available at https://github.com/WwZzz/easyFL.

This paper proposes the first tracklet proposal network, named PC-TCNN, for Multi-Object Tracking (MOT) on point clouds. Our pipeline first generates tracklet proposals, then refines these tracklets and associates them to generate long trajectories. Specifically, object proposal generation and motion regression are first performed on a point cloud sequence to generate tracklet candidates. Then, spatial-temporal features of each tracklet are exploited and their consistency is used to refine the tracklet proposal. Finally, the refined tracklets across multiple frames are associated to perform MOT on the point cloud sequence. The PC-TCNN significantly improves the MOT performance by introducing the tracklet proposal design. On the KITTI tracking benchmark, it attains an MOTA of 91.75%, outperforming all submitted results on the online leaderboard.

Recent years have witnessed the growing research interest in the Context-Aware Recommender System (CARS). CARS for Web service provides opportunities for exploring the important role of temporal and spatial contexts, separately. Although many CARS approaches have been investigated in recent years, they do not fully address the potential of temporal-spatial correlations in order to make personalized recommendation. In this paper, the Context-Aware Services Recommendation based on Temporal-Spatial Effectiveness (named CASR-TSE) method is proposed. We first model the effectiveness of spatial correlations between the user's location and the service's location on user preference expansion before the similarity computation. Second, we present an enhanced temporal decay model considering the weighted rating effect in the similarity computation to improve the prediction accuracy. Finally, we evaluate the CASR-TSE method on a real-world Web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, and thus it is much more effective than traditional recommendation techniques for personalized Web service recommendation.

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.

Deep learning techniques have been widely applied to traffic flow prediction, considering underlying routine patterns, and multiple context factors (e.g., time and weather). However, the complex spatio-temporal dependencies between inherent traffic patterns and multiple disturbances have not been fully addressed. In this paper, we propose a two-phase end-to-end deep learning framework, namely DeepSTD to uncover the spatio-temporal disturbances (STD) to predict the citywide traffic flow. In the STD Modeling phase, we propose an STD modeling method to model both the different regional disturbances caused by various region functions and the spatio-temporal propagating effects. In the Prediction phase, we eliminate the STD from the historical traffic flow to enhance the leaning of inherent traffic patterns and combine the STD at the prediction time interval to consider the future disturbances. The experimental results on two real-world datasets demonstrate that DeepSTD outperforms the state-of-the-art methods.

Next location prediction plays an essential role in location-based applications. Many works have been employed to predict the next location of an object (e.g. a vehicle), given its historical location records. However, existing methods have not fully addressed the importance of contextual features, such as the short-term traffic flows. In this paper, we propose a deep learning-based model to incorporate contextual features into next location prediction. First, we conduct the similarity mining among candidate locations. Second, we model contextual features among trajectories, including both periodical patterns and dynamic features of trajectories. Third, we adopt both CNN and bidirectional LSTM networks to predict next location in each trajectory with contextual information. Intensive experiments on 197 million vehicle license plate recognition (VLPR) records in Xiamen, China, demonstrate that the proposed method outperforms several existing methods.

Existing ridesharing services have focused on on-demand trip matching, which resembles traditional taxi dispatching. This may encourage more private vehicles on the road, which aggravate traffic congestions in peak hours rather than alleviating them. We propose CommuteShare, a novel ridesharing service for daily commuters that encourages long-term ridesharing among commuters with similar commuting patterns, to increase the traffic efficiency in peak hours. We first identify commuting private vehicles (CPVs) from traffic records and model their commuting patterns. We then design a dynamic model to formulate the intention level of a CPV driver to offer a ride based on the spatio-temporal convenience and dynamic traffic conditions. Based on the commuting patterns of the CPVs and the dynamic model of the CPV drivers, we propose a ridesharing algorithm to compute ridesharing matches among CPVs. We perform extensive experiments on three real-world cross-domain urban big datasets from a major city of China. Experimental results show that, using the proposed CommuteShare service, over 5,300 private vehicles can be reduced daily on average during morning peak hours, with a reduction of 7-minute average waiting time for the riders.

Unlicensed taxis are widely considered as major obstacles to city traffic regulation and public safety. Thus, many governments have issued restrictions for car-hailing services and alleged that the use of unlicensed vehicles was illegal. However, it is very challenging that traffic administrative enforcements face limited manpower to prohibit unlicensed taxis, due to costly and time-consuming procedure of on-site evidence collection. In this paper, we propose an effective service to incorporate human mobility mechanism into unlicensed taxis detection from massive city-wide vehicles. We first extract 276 spatio-temporal features, which are grouped into two categories, including daily behaviors and sustainable behaviors to capture the mobility characteristics of unlicensed taxis. Second, we investigate the detection accuracy of three machine learning techniques, viz. support vector machines, decision tree, and logical regression. We illustrate our approach using real-world vehicle license plate recognition dataset in Xiamen, China, which contains 336 million passing records for 6.2 million vehicles filmed by 439 devices in August 2016. Experimental results reveal that LR outperforms SVM and DT in prediction accuracy and F-score measurement, while SVM is capable of identifying the largest number of unlicensed taxis.

Recent years have witnessed a growing interest in context-aware recommender system (CARS), which explores the impact of context factors on personalized Web services recommendation. Basically, the general idea of CARS methods is to mine historical service invocation records through the process of context-aware similarity computation. It is observed that traditional similarity mining process would very likely generate relatively big deviations of QoS values, due to the dynamic change of contexts. As a consequence, including a considerable amount of deviated QoS values in the similarity calculation would probably result in a poor accuracy for predicting unknown QoS values. In allusion to this problem, this paper first distinguishes two definitions of Abnormal Data and True Abnormal Data, the latter of which should be eliminated. Second, we propose a novel CASR-TADE method by incorporating the effectiveness of True Abnormal Data Elimination into context-aware Web services recommendation. Finally, the experimental evaluations on a real-world Web services dataset show that the proposed CASR-TADE method significantly outperforms other existing approaches.

Context-Aware Recommender System (CARS) aims to not only recommend services similar to those already rated with the highest score, but also provide opportunities for exploring the important role of temporal, spatial and social contexts for personalized web services recommendation. A key step for temporal-based CARS methods is to explore the time decay process of past invocation records to make the Quality of Services (QoS) prediction. However, it is a nontrivial task to model the temporal effects on web services recommendation, due to the dynamic features of contextual information in view of temporal spatial correlations. For instance, in location-aware services recommendation, the user's geographical position would change very frequently as time goes on. In this paper, we propose a Context-Aware Services Recommendation based on Temporal Effectiveness (CASR-TE) method. Inspired by existing time decay approaches, we first present an enhanced temporal decay model combining the time decay function with traditional similarity measurement methods. Then, we model temporal spatial correlations as well as their impacts on the user preference expansion. Finally, we evaluate the CASR-TE method on WS-Dream dataset by evaluation matrices of both RMSE and MAE. Experimental results show that our approach outperforms several benchmark methods with a significant margin.

Context-Aware Recommender System aims to recommend items not only similar to those already rated with the highest score, but also that could combine the contextual information with the recommendation process. Existing context-aware Web services recommendation methods directly use context as a "filter" to discard services that may conflict with the current user's preference. However, the discarded services may be valuable for another user or for the same user under a new context, as one man's trash may be another's treasure. We assume that failing to handle the contextual reasons behind the user preference may introduce inaccurate recommendation, and even significant biases in recommendation. In this work, we propose a novel method dubbed CASR-UP, which aims to exploit the contextual factors of the user preference to improve Quality of Service (QoS) prediction and services recommendation accuracy. Our method consists of three stages: 1) context-aware similarity mining to get the set of users having similar context with the current user, 2) data filtering based on user preference in current context so as to get the invocation records of the services corresponding to the current user's preference, 3) Web services QoS prediction, recommendation and evaluation by Bayesian Inference. Experimental results on WS-Dream dataset is evaluated by both RMSE and MAE. The results show the proposed method improves prediction accuracy and outperforms the compared methods.

Scientific workflows (SWFs) aim to automate cooperative design through compilation of known sequences of actions for routine procedures. However, current SWF systems lack the ability, in the one hand, to capture the context in which a SWF is designed and developed, and, in the other hand, to deliver a real-time assistance to help designers to make effective decisions in the selection of relevant SWFs for their problem. We propose a context-oriented framework for improving cooperative SWF design. This framework allows making context explicit and realizing the contextualization of SWFs in a SWF repository (and thus sharable with other scientists). Context is made explicit thanks to Contextual Graphs (CxGs), a formalism for representing uniformly all the ingredients of a cooperative SWF design process. Thus, scientists can customize information and formalize their research and strategies in a shared context. We use the context-oriented framework in an application in virtual screening. Finally, context-based formalisms, such as CxGs, appear as the key element to enhance users' cooperation during the SWF design phase.

SWF, short for scientific workflow, has recently emerged as a paradigm for orchestrating large-scale e-Science applications. SWF specification connects workflow designer with workflow engine, which makes it act ass one of the key components in SWF systems. We adopted BPEL as the Gird-based SWF specification because of its potential benefits to promote SWF sharing and reproducibility. However, grid-based SWF has unique features, which pure BPEL is not capable to handle. Appropriate ways to extend BPEL to fulfill the special needs should be found. We concluded three most necessary requirements and three kinds of alternative methods to extend BPEL within China Grid Support Platform, and implemented the method of adding additional abstractions upon BPEL to enhance user experience. Our work and findings suggest that our extension approach is feasible and would manifest potential advantage to SWF systems and finally innovate in the e-Science research and applications.