The proliferation of vehicles equipped with onboard sensors has positioned vehicular crowdsensing (VCS) as a promising approach for urban sensing and monitoring. These mobile sensors offer a unique opportunity to capture comprehensive urban information. Consequently, the selection of appropriate participants is critical for the success of vehicular crowdsensing campaigns. The effectiveness of such campaigns is typically assessed based on the spatial and temporal coverage of the road network. However, existing research predominantly focuses on spatial coverage, often overlooking the temporal aspects of data collection. In this study, we bridge this gap by introducing a temporal coverage framework that accounts for both sensing frequency and duration. We formulate an optimization problem aimed at minimizing the overall recruitment cost, adhering to the principle of Epoch-based Participant Recruitment (EPR) and utilizing the deterministic trajectory information of vehicles. Our approach is validated through simulation experiments using a real-world dataset of vehicle trajectories, demonstrating superior performance compared to existing participant recruitment strategies.

Traffic demand prediction is of great importance for traffic management, yet it is also a challenging problem since the traffic data are usually with complex spatial-temporal dependencies and nonlinear relationships. In this paper, to better characterize and utilize the spatial and temporal features, we propose a Multi-dimensional Graph Convolutional Network (M-GCN) to capture dynamic spatial-temporal dependence of traffic data. M-GCN captures the explicit spatio-temporal dependencies by establishing a spatial adjacency graph and a temporal adjacency graph, and captures the hidden spatiotemporal dependencies by establishing a spatial adaptive graph and a temporal adaptive graph. We leverage graph convolutional networks for complex spatial and temporal dependencies modeling and design a gated fusion module to obtain the interactive spatial-temporal dependence. Besides, an attention mechanism is applied to alleviate the error propagation problem in long-term traffic prediction. Experimental results on two real-world traffic datasets demonstrate the superiority of M-GCN. The proposed M-GCN outperforms baseline methods by up to 4.6% improvement in MAE on the dataset TaxiNYC, and the training time and predicting time of $\mathrm{M}-\mathrm{GCN}$ are reduced by nearly half.

In this paper, we devote to aggregating longitudinal and multi-modal information from heterogeneous neighbors to obtain an accurate node embedding on the dynamic heterogeneous graph. Recently, Heterogeneous Graph Neural Networks (GNNs) have attracted extensive attention in fraud detection. However, when faced with longitudinal and multi-modal data such as health insurance records, existing GNN-based fraud detectors always discard the multi-modal information (e.g., medication and treatment) of heterogeneous neighbors and ignore the inconsistent claimer behavior in the longitudinal records. To fully utilize the information, we represent the records in the form of a dynamic heterogeneous graph, and propose Hierarchical Multi-modal Fusion Graph Neural Network (HMF-GNN) which learns not only topological information, but also embeddings of longitudinal and multi-modal entities to improve the performance of fraud detection. Experimental results on two real-world health insurance datasets demonstrate that HMF-GNN outperforms state-of-the-art graph embedding methods and GNN-based fraud detectors.

BACKGROUND: Accurate precision approaches have far not been developed for modeling mortality risk in intensive care unit (ICU) patients. Conventional mortality risk prediction methods can hardly extract the information in longitudinal electronic medical records (EHRs) effectively, since they simply aggregate the heterogeneous variables in EHRs, ignoring the complex relationship and interactions between variables and the time dependence in longitudinal records. Recently deep learning approaches have been widely used in modeling longitudinal EHR data. However, most existing deep learning-based risk prediction approaches only use the information of a single disease, neglecting the interactions between multiple diseases and different conditions. RESULTS: In this paper, we address this unmet need by leveraging disease and treatment information in EHRs to develop a mortality risk prediction model based on deep learning (DeepMPM). DeepMPM utilizes a two-level attention mechanism, i.e. visit-level and variable-level attention, to derive the representation of patient risk status from patient's multiple longitudinal medical records. Benefiting from using EHR of patients with multiple diseases and different conditions, DeepMPM can achieve state-of-the-art performances in mortality risk prediction. CONCLUSIONS: Experiment results on MIMIC III database demonstrates that with the disease and treatment information DeepMPM can achieve a good performance in terms of Area Under ROC Curve (0.85). Moreover, DeepMPM can successfully model the complex interactions between diseases to achieve better representation learning of disease and treatment than other deep learning approaches, so as to improve the accuracy of mortality prediction. A case study also shows that DeepMPM offers the potential to provide users with insights into feature correlation in data as well as model behavior for each prediction.

Modern vehicles are gradually becoming powerful mobile sensing, communication, computing and storage platforms, which bring about the concept of vehicular urban sensing that leverages sensor nodes as an effective and affordable solution for large-scale and fine-grained sensing. And there is a trend to combine the vehicular sensing and outsourcing technologies to solve the large-scale urban road sensing problem. However, how to select appropriate participated vehicles, how to least interrupt the original routes of vehicles, and how to actively maximize the benefit of the sensing remain challenging problems. In this paper, we introduce a Crowdsourced Vehicular Sensing (CVS) framework based on more realistic assumptions of the vehicular sensing, which consists of three steps: vehicle recruitment, candidate path calculation, and path computing. We define amaximal weighted sensing paths(MWSP) problem, which is NP-Complete, in vehicular crowdsensing scenario and use heuristic methods to speed up the solving process for large-scale crowdsensing in urban road networks. The MWSP problem is formulated as a maximal satisfiability (MaxSAT) problem, and aleast-interruptedurban sensing strategy is adopted. So trips are least disrupted when conducting the sensing tasks, which would increase the drivers’ willingness to participate in urban sensing. Experiments based on real-world road-network and historical origin-destination datasets verify the effectiveness of the proposed method. The results show that the proposed algorithm outperforms other solutions and it can solve the vehicular crowdsensing problem effectively and efficiently.

The flexible transit service reflects a trend of demand on the flexibility and convenience in urban public transport systems, within which the vehicle scheduling and passenger insertion are two challenging issues. Especially, finding the optimal solution for a flexible transit system can be viewed as an extension of the traveling salesman problem which is NP-complete. Yet most of the existing research mainly focuses on one aspect, i.e. route planning, stop selection or vehicle scheduling, where a combined integration and optimization of the whole system is largely neglected. In this paper, we propose a data-driven flexible transit system that integrates the origin-destination insertion algorithm and the milp-based (mixed-integer linear programming) scheduling scheme. Specifically, stops are mined from the historical datasets and some stops act as$backbone$stops that should be visited by the vehicles; and a heuristic backbone-based origin-destination insertion algorithm is proposed to schedule the routing path of vehicles, where the time loss caused by the optimal insertion positions is calculated for the vehicles to decide whether to accept the requests or not when constructing a path for the flexible routes. Moreover, a vehicle scheduling model based on milp is proposed to minimise the gap between the passenger flow and available seats. The proposed flexible transit systems are simulated in real-world taxi datasets, and experimental results show that the proposed flexible transit system can effectively increase the delivery ratio and decrease the passengers’ waiting time compared with existing methods.

In rider demand responsive systems riders submit requests to demand transit services and the incoming requests and vehicles are matched by the system. This demand-responsive transport problem is viewed by existing research as a kind of spatial matching problem between vehicles and riders. However, existing schemes mainly focus on maximizing the number of matching pairs. It neglects other factors like the length of pickup trajectories and riders’ waiting time. And the matching is not revocable, which loses the chance for further optimizations. Moreover, there is still not much work on handling and matching the appointment-based requests that play a key role in the demand-responsive transport market. In this article, we propose an algorithm called BMCF (Bipartite Minimal-Cost Flow) to solve the taxi-rider matching problem with appointment-based rider requests on a time-dependent road network. Unlike existing solutions that map the problem into the max flow problem, we transform the optimal matching to aminimal cost flow problemwhich aims to maximize a utility value and could be solved efficiently. Riders and vehicles are modeled as vertices in a bipartite graph, and factors like the length of pickup trajectories, riders’ waiting time, etc. are abstracted as utilities and denoted as weights of the edges which are taken into account during the matching process. To the best of our knowledge, the proposed scheme is the first to efficiently integrate and process both the real-time and appointment-based requests within the same framework, and the assignments between vehicles and requests could be dynamically adjusted and revoked to maximise the overall utility. Experimental results show that the proposed algorithm can effectively increase the appointment-based matching ratio and decrease the riders’ waiting time (> 10.4%) and vacant vehicles’ picking up time (> 60.9%) compared with existing schemes, while at the cost of acceptable increase on the running time.

Semi-supervised cluster ensemble usually introduces a small amount of supervision in the first stage of cluster ensemble, i.e., ensemble generation, by performing many runs of semi-supervised clustering algorithms. However, it is neither efficient in terms of computational complexity, nor flexible in a dynamic learning environment where limited supervision changes over time. In this article we propose a new framework which generates base partitions in an unsupervised manner and attributes different weights to each cluster of the base partitions. The weighting scheme considers both the internal validation measures of clustering and the degrees of satisfaction of pairwise constraints. A weighted co-association matrix based consensus approach is then applied to achieve a final partition. To handle high-dimensional data, we generate base partitions using k-means with both random sampling and random subspace techniques. The new framework retains a high accuracy, and is efficient since it avoids performing semi-supervised clustering in ensemble generation and the complexity of the weighting scheme is independent of the number of instances in a dynamic environment. It is more adaptive than the traditional approach because it does not require rerunning semi-supervised clustering algorithms when the limited supervision changes. Empirical results on 12 datasets demonstrate that it is also more robust to noisy constraints.

Recently, an increased amount of effort has been focused on optimizing the selection of routes for taxis, as part of the development of smart urban environments, and the increase of the accumulated trajectory data sets. One challenging issue is to match and recommend appropriate cruising routes to taxis, as most taxis cruise on streets aimlessly looking for passengers. Drivers encounter lots of difficulty in optimizing their cruise routes and hence increasing their incomes, and such inability not only decreases their profit but also increases the traffic load in urban cities. In this paper, the concept of urban traffic Coulomb's law is coined to model the relationship between taxis and passengers in urban cities, based on which a route recommendation scheme is proposed. Taxis and passengers are viewed as positive and negative charges. It first collects useful information such as the density of passengers and taxis from trajectories, then calculates the traffic forces for cruising taxis, based on which taxis are routed to optimal road segments to pick up desired passengers. Different from existing route recommendation methods, the relationship among taxis and passengers are fully taken into account in the proposed algorithm, e.g., the attractiveness between taxis and passengers, and the competition among taxis. Moreover, real-time dynamics and geodesic distances in road networks are also considered to make more accurate and effective route recommendations. Extensive experiments are conducted on the road network using the trajectories generated by approximately 5,000 taxis to verify the effectiveness, and evaluations demonstrate that the proposed method outperforms existing methods and can increase the drivers' income more than 8%.

As people acquire much more personal information as a result of personal and work activities, the management of these information becomes a serious problem and an important research issue. Modeling personal desktop activities and identifying them are two basic problems for supporting activity-based operations. To the best of our knowledge there is no literature on formalizing and identifying desktop activity from personal information management perspective. There are a number of challenges to this work, including the fact that people exhibit personalized behaviors, have individual interests, needs and resources, no available experimental data set, etc. In this paper, we perform a user experiment to learn about user desktop activities in a personal information management context. We collected information access activities in a naturalistic setting and propose a conceptual activity model by analyzing features of user behaviors at their desktop computers. We present an effective and efficient method of automatically identifying desktop activities. To evaluate performance of our method, we develop a prototype system to collect real users activities, and evaluate our methods for identifying activities. The results verify the effectiveness and efficiency of our methods.

Entity search is a new search pattern that return related entities to users rather than amounts of web pages containing mass and messy information. It is also a challenging research topic because it is difficult to understand the meaning of users' input and identify the entities from the messy web pages. In this paper, we propose an entity search pattern based on online encyclopedias and define it as MMK search(Multi-modifier Search), which means the input text by people only includes one kernel concept and multiple modifiers. We propose a solution framework to solve this kind of search, and propose a method to identify expected entities based on well-utilized online encyclopedias. To evaluate the methods, we create an experimental data set and a baseline under the help of participants, the results verified the effectiveness of our methods.

Tables and spreadsheets on the Internet often contain valuable information, but are created by people who have different individuation. As a result, the similar data are often issued with different structures. This limits the integration of such tables. This paper aims to overcome this problem by automatically analyzing the structure area and propose the method transforming the tables into formal relational tables. We propose the methods on identifying structure area, modeling the table structure based on tree and methods to generate the 1NF schema of the original table. We proved the correctness of the method in semantic and the experiment results with tables from different areas demonstrate the effectiveness of our method.

Abstract Mobile opportunistic network is a kind of delay‐tolerant network that consists of mobile wireless‐enabled nodes and adopts the form of opportunistic transmissions. When nodes are within the radio range, data and contents could be exchanged among the nodes to meet users' requests or subscriptions. Yet because of unpredictable node movements and varied users' interests, the sharing of contents is a challenging problem in mobile opportunistic networks. In this paper, we propose an efficient multi‐resolution content sharing algorithm in mobile opportunistic networks. The algorithm takes data granularity into account and attaches to each content segment a priority weight that combines factors of the node contact pattern and the match between the requests and contents when nodes are in contact. Then, on the basis of the priority weight, the most valued contents of proper resolution level are forwarded to the peer nodes for sharing. Our algorithm also calculates an importance weight for the contents stored in the cache, and content segments with the smallest importance would be transformed into lower resolution to free storage space and hence improve the overall usage of the storage. Extensive experimental results show that our algorithm performs well under extensive conditions. It increases the content delivery rate with little extra cost on message transmissions and delay of requests compared with other algorithms. To the best of our knowledge, this work is the first step on the research on multi‐resolution content sharing in mobile opportunistic networks. Copyright © 2014 John Wiley & Sons, Ltd.

Recently there have been growing interests in the applications of wireless sensor networks such as traffic tracking, environmental surveillance, and network monitoring. In these applications, the exploration of the relationship and linkage of sensing data with other data sources can be naturally expressed by the external join, where the sensory tuples join with an external table at the base station. However, executing such kind of join queries in a highly distributed and resource-constraint sensor network is a challenging task. In this paper, we propose a partition-based algorithm called NEJA (in-Network External Join Algorithm) for the external join processing in sensor networks. NEJA organizes the sensory data of the network through an optimized "value-to-storage" mapping, according to which each storage point stores the tuples that belong to the same subrange on the joint attribute. Then the subrange of each storage point is further partitioned into unit ranges, and tuples in the same unit range wisely choose their joining point that incurs the least communication cost based on a cost metric according to the latest historical statistics. Also, NEJA adopts some optimization techniques to handle the changes of sensory data and uses approximate approaches to cut down the maintenance cost of the mechanism. The experimental results indicate that our scheme is effective in reducing the amount of transmissions for the real time external join processing, especially when the external table has a relatively large size.

Clustering is an effective topology control and communication protocol in wireless sensor networks ("sensornets"). However, the harsh deployed environments, the serious resource limitation of nodes, and the unbalanced workload among nodes make the clustered sensornets vulnerable to communication faults and errors, which undermine the usability of the network. So mechanisms to improve the robustness and fault-tolerance are highly required in real applications of sensornets. In this paper, a distributed fault-tolerant mechanism called CMATO (Cluster-Member-based fAult-TOlerant mechanism) for sensornets is proposed. It views the cluster as an individual whole and utilizes the monitoring of each other within the cluster to detect and recover from the faults in a quick and energy-efficient way. CMATO only needs the local knowledge of the network, relaxing the pre-deployment of the cluster heads and a k-dominating set (k>1) coverage assumptions. This advantage makes our mechanism flexible to be incorporated into various existing clustering schemes in sensornets. Furthermore, CMATO is able to deal with failures of multiple cluster heads, so it effectively recovers the nodes from the failures of multiple cluster heads and the failures of links within the cluster, gaining a much more robust and fault-tolerant sensornets. The simulation results show that our mechanism outperforms the existing cluster-head based fault-tolerant mechanism in both fault coverage and energy consumption.

Recently there have been growing interests in the applications of wireless sensor networks. Given a query point, which is a value, find a set of K nodes whose values are nearest to this point. We call this query the value-based KNN (v-KNN) query. v-KNN is a challenging query in wireless sensor networks because the network is highly distributed and global knowledge of the values are needed before the result set could be constructed. In this paper, we propose a new algorithm called KVC (v-KNN queries based on value clustering ) to process the v-KNN queries in wireless sensor networks in an energy efficient way. KVC takes advantage of the rich resources in the base station to do some optimizations to filter the irrelevant nodes and push down the v-KNN query into the network. The aim of KVC is to reduce the total communication cost as much as possible while processing the v-KNN queries correctly and efficiently. Simulation results on both realistic and synthetical data sets show that KVC outperforms the central processing in both average and hotspot energy consumption in wireless sensor networks.