Sparse Urban CrowdSensing (Sparse UCS) is a practical paradigm for completing full sensing maps from limited observations. However, existing methods typically rely on a time-discrete assumption, where data is considered static within fixed intervals. This simplification introduces significant errors as real-world data changes continuously. To address this, we propose a framework for time-continuous data completion. Our approach, Time-Aware Mamba-based Deep Matrix Factorization (TIME-DMF), leverages the Mamba architecture as a powerful temporal encoder. Crucially, we enhance Mamba with a novel time-aware mechanism that explicitly incorporates the actual, often irregular, physical time intervals between observations into its state transitions. This allows our model to accurately capture true temporal dynamics and generate high-fidelity data for any queried moment in time through a query-generate mechanism. Extensive experiments on five diverse sensing tasks demonstrate that TIME-DMF significantly outperforms state-of-the-art methods, validating the superiority of the time-continuous paradigm for Sparse UCS.

Xingyu Lin, Yilin Wen, Du Su, En Wang, Wenbin Liu, Zhonghou Lv, Jinchang Hou, Chenfu Bao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Configuration management in large-scale cloud networks is increasingly challenging due to vendor heterogeneity, diverse configuration interfaces, and rapid configuration evolution across the network life cycle. Existing approaches rely heavily on vendor- and interface-specific templates and scripts, which are difficult to validate, costly to maintain, and scale poorly. We introduce AliYANG, a YANG-based configuration modeling framework that unifies configuration representation across vendors and management interfaces. It extends YANG to capture CLI semantics and derives a vendor-agnostic core model that separates configuration semantics from vendor-specific implementations. We further present NetCMDB, the production software infrastructure for AliYANG, which compiles models into typed configuration objects and supports end-to-end, model-driven configuration workflows. As networks evolve, manually constructing and maintaining models becomes a bottleneck. We incorporate LLM-assisted automation to facilitate vendor model augmentation, core model design, and bidirectional translation code generation. We report our three-year production deployment experience managing hundreds of thousands of devices, present evaluation results and case studies, and share lessons from operating a model-driven configuration system at cloud scale.

Abstract Data quality and budget are two major concerns in large-scale urban Mobile Crowdsensing (MCS) technologies. Traditional MCS research primarily measures data quality based on sensing coverage, without fully considering the importance of the data for downstream tasks. As a result, in Sparse Mobile Crowdsensing (SMCS), existing studies often focus on selecting the subregions most valuable for data inference, overlooking the ultimate goal of data collection, which is to support subsequent decision-making. With the rise of Embodied AI, a core challenge is how to actively collect key information that is most helpful for decision-making under conditions of sparse data and high costs. For example, in urban air quality monitoring, insufficient sensor deployment may cause critical polluted regions to go unobserved, undermining public health decisions. Similarly, sparse traffic data can lead to errors in autonomous driving systems, such as flawed route planning or hazard detection failures. As a feasible data collection paradigm for Embodied AI, existing SMCS subregion selection methods often ignore the relevance of data to tasks, thereby affecting decision-making in Embodied AI. To address this, we design a Sparse Mobile Crowdsensing active perception framework that selects the most valuable subregions for decision-making, considering the context of downstream tasks. Furthermore, existing SMCS subregion selection methods usually focus only on selecting the optimal subregion, whereas Embodied AI requires selecting a set of subregions. Choosing only the optimal subregion may overlook information overlap between subregions, leading to wasted data collection costs and reducing the perception efficiency of Embodied AI. This paper proposes an active acquisition strategy that takes into account parameter uncertainty and probabilistic margins, enabling the selection of an optimal set of subregions. Since this is an NP-hard problem, we approximate the proposed strategy using a greedy algorithm and provide performance guarantees for this approximation through theoretical proofs. Finally, we conduct experiments on three real-world datasets to validate the effectiveness of our proposed method. Experimental results show that when the data missing rate exceeds 60%, MBBALD starts to outperform other algorithms. On the traffic flow dataset, it achieves over 4% higher accuracy than the second-best algorithm at a 50% missing rate. Its advantage becomes more evident as the number of selected points increases, surpassing the second-best method by over 3% with more selected points.

Sparse Mobile CrowdSensing (SMCS) is an emerging distributed data collection framework. As one of the core methods,data completion uses the collected data to fill in the missing data. However, this approach inevitably poses significant privacy risks, since the traditional data completion methods require users' time and location information. In this paper, we propose a federated learning-based distributed data completion framework, which employs matrix factorization (MF) for local completion model training and federated learning to aggregate parameters of the MF model. This enables the construction of a global completion model without requiring private data, thereby mitigating privacy concerns. To address the challenges posed by the sparsity and asynchrony of distributed data, we incorporate time-aware deep neural network architectures with an asynchronous mechanism to enable federated training under irregularly gathered local data. Experimental results demonstrate that the proposed model achieves high data completion accuracy while ensuring robust privacy protection.

Sparse crowdsensing tackles data incompleteness through sparse sensing coupled with inference-based completion. Yet prevailing solutions face three inherent constraints: (i) Spatiotemporal completion methods demand extensive historical/cross-domain data for source-domain pretraining and target-domain fine-tuning; (ii) Such dependency causes catastrophic performance degradation in cold-start scenarios lacking target-domain samples, a setting that remains largely unaddressed by existing data-dependent frameworks; (iii) Resource-intensive pretraining-fine-tuning pipelines hinder lightweight deployment. To overcome these challenges, this paper proposesMappingTransfer Learning-basedSpatiotemporalCompletion method (MapT-STC), a one-shot spatiotemporal completion framework that advances beyond few-shot approachesCorrelatedDataFusion forMatrix Completion (CDFMC) by featuring a remodeled pretraining-finetuning paradigm. Departing from conventional approaches, MapT-STC implements a dual-path learning system: it simultaneously trains a target-specialized model for domain-specific features and a cross-domain generalizable model viaCorrelationAlignment (CORAL). The framework explicitly aligns feature distributions by decomposing spatiotemporal data into separable temporal patterns and spatial structures, thereby enhancing the quality of domain adaptation. Ultimately, Kalman-filter-driven fusion integrates predictions from both specialized and generalized models for optimal completion. Experiments demonstrate MapT-STC's superiority under cold-start conditions: reducing initial error by over 30% and accelerating convergence time by at least 7.4% compared to state-of-the-art baselines, using only one target-domain training sample.

Sparse CrowdSensing has emerged as a promising paradigm for data collection, which utilizes mobile devices to gather partial sensing data and infer the remaining ones. Most existing studies focus on data inference. However, they often overlook the impact of unforeseen circumstances. These perturbations can lead to many issues in the data inference process such as over-correction (adjustments are excessively applied), perturbation diffusion (errors affecting related data), and data bias (overall trends in inferred data deviating from the truth). To address the aforementioned challenges, we propose a novel approach called Gradual Matrix Completion (GMC) for handling perturbations in Sparse CrowdSensing. This method distinguishes itself by inferring adjacent unsensed data and using it as a basis for ongoing optimization, gradually completing the entire data matrix. Through the gradual inference process, GMC mitigates perturbation diffusion by continuously refining feature selection, thus preserving and reconstructing essential features. Moreover, GMC concentrates on both intra-area and inter-area spatio-temporal relationships, progressively enhancing its understanding of local and global dependencies. Evaluation of the GMC approach on four diverse real-world datasets highlights its robust capability to complete perturbed data and cope with noise in Sparse CrowdSensing.

Sparse Mobile CrowdSensing (MCS) is a cost-effective paradigm which recruits users to sense only a small amount of data and infer the rest ones. The existing works usually provide rewards for users to cover their sensing costs. Compared with the costs, we actually need to pay for the value of data, which is a new and valuable perspective of data pricing. However, in Sparse MCS, we can hardly estimate the value of data accurately, as it depends not only on the data itself but also on its contribution to inference. Moreover, the sensing data is naturally noisy and its spatiotemporal information may be invisible until we pay for it, which makes the data pricing more challenging. To this end, we investigate the value of sensing data, and propose a data pricing framework for Sparse MCS. We first present the sampling-based uncertainty quantification to evaluate the value of data, with the aim to pay for the intrinsic data values but not the indirect costs. As a byproduct, the noise can be dealt with naturally by such sampling-based methods. Further considering the availability of spatiotemporal information, we also study three cases of data pricing, i.e, paying for sensed data, spatiotemporal information, and next data. Extensive experiments on five real-world data sets verify the effectiveness of our proposals.

Human preference alignment (HPA) aims to ensure Large Language Models (LLMs) responding appropriately to meet human moral and ethical requirements. Existing methods, such as RLHF and DPO, rely heavily on high-quality human annotation, which restrict the efficiency of iterative online model refinement. To address the inefficiencies of human annotation acquisition, iterated online strategy advocates the use of fine-tuned LLMs to self-generate preference data. However, this approach is prone to distribution bias, because of differences between human and model annotations, as well as modeling errors between simulators and real-world contexts. To mitigate the impact of distribution bias, we adopt the principles of adversarial training, framing a zero-sum two-player game with a protagonist agent and an adversarial agent. With the adversarial agent challenging the alignment of protagonist agent, we continuously refine the protagonist’s performance. By utilizing min-max equilibrium and Nash equilibrium strategies, we propose Indirect Online Preference Optimization (IOPO) mechanism that enables the protagonist agent to converge without bias while maintaining linear computational complexity. Extensive experiments across three real-world datasets demonstrate that IOPO outperforms state-of-the-art alignment methods in both offline and online scenarios, evidenced by standard alignment metrics and human evaluations. This innovation reduces the time required for model iterations from months to one week, alleviates distribution shifts, and significantly cuts annotation costs.

Sequential point-of-interest (POI) recommendation aims to recommend the next POI for users in accordance with their historical check-in information. However, few attempts treat timestamps of check-ins as a core factor for sequence models, leading to insufficient insight into user behavior and subsequently suboptimal recommendations. To address these limitations, we propose to assign equal importance to both POIs and their timestamps, shifting the point of view to recommend the next POI and predict the corresponding timestamp. Along these lines, we present the Time-Aware POI Recommender with Timestamp Prediction (TAPT), a multi-task learning framework for explainable POI recommendations. Specifically, we begin by decoupling timestamps into multi-dimensional vectors and propose a timestamp encoding module to explicitly encode these vectors. Additionally, we design a specialized timestamp prediction module built on the traditional sequence-based POI recommender backbone, effectively learning the strong correlation between POIs and their corresponding timestamps through these two modules. We evaluated the proposed model with three real-world LBSN datasets and demonstrated that TAPT achieves comparable or superior performance in POI recommendation compared to the baseline backbone. Besides, TAPT can not only recommend the next POI, but predict the corresponding timestamp in the future.

Sequential models aim to predict future interactions based on users' historical interaction sequences. Traditional sequential methods primarily focus on capturing intra-historical sequence dependencies, overlooking the influence of unobserved confounders in recommendation scenarios. Recent studies incorporate time as additional information helps the model capture dynamic user preferences. However, time is just the external manifestation of the influence of confounders but not the actual cause of the dynamic of user preference. Additionally, improperly integrating time with item embeddings can obstruct the model's ability to capture sequence dependencies. To address these challenges, we first revisit the sequential recommendation problem from a causal perspective and incorporate confounders as a new task. We propose a new framework—Flow-based Time-aware Causal Structure for Sequential Recommendation (FCSRec)—explicitly incorporating unobserved confounders' influence in the recommendation process. Specifically, we use Normalizing Flows to learn the causal graph of confounders and incorporate time information as conditional info to capture confounders' time-sensitive representations. To balance the influence of confounders and sequence dependencies, we introduce a classifier-free training paradigm by randomly masking the influence of confounders during training to encourage the model to learn both sequence dependencies and confounders' influence equally. We validate FCSRec on manifold real-world datasets, and experimental results show that FCSRec outperforms several state-of-the-art methods in recommendation performance. Our code is available at Code-link.

Sparse Mobile CrowdSensing has emerged as a practical method for data collection, recruiting mobile users to collect partial data and leveraging spatiotemporal correlations to infer the missing data. To improved the QoS of crowdsourced data, existing methods typically assume that the scales of collected data are similar. However, in real-world scenarios, the diversity of user devices results in data collections that vary in scale. More importantly, the collected coarser-scale data points often do not perfectly correspond to multiple finer-scale data points, resulting in highly complex compositional relationships and posing significant challenges for multi-scale data completion. To address these challenges, this paper proposes a multi-scale data completion framework designed to process and integrate multi-scale data with non-aligned compositional relationships. We first align features across scales using the least common multiple scaling, then enhance the interaction and integration of data across scales through a bidirectional processing strategy and modified Mamba architectures, specifically ST-Mamba and Cross-Mamba. Evaluated on six real-world datasets, our study demonstrates the effectiveness of the proposed framework in handling multi-scale data completion challenges, particularly when dealing with non-aligned compositional relationships.

Positive-Unlabeled (PU) learning refers to a specific weakly-supervised learning paradigm that induces a binary classifier with a few positive labeled instances and massive unlabeled instances. To handle this task, the community has proposed dozens of PU learning methods with various techniques, demonstrating strong potential. In this paper, we conduct a comprehensive study to investigate the basic characteristics of current PU learning methods. We organize them into two fundamental families of PU learning, including *disambiguation-free empirical risks*, which approximate the expected risk of supervised learning, and *pseudo-labeling methods*, which estimate pseudo-labels for unlabeled instances. First, we make an empirical analysis on disambiguation-free empirical risks such as uPU, nnPU, and DistPU, and suggest a novel risk-consistent set-aware empirical risk from the perspective of aggregate supervision. Second, we make an empirical analysis of pseudo-labeling methods to evaluate the potential of pseudo-label estimation techniques and widely applied generic tricks in PU learning. Finally, based on those empirical findings, we propose a general framework of PU learning by integrating the set-aware empirical risk with pseudo-labeling. Compared with existing PU learning methods, the proposed framework can be a practical benchmark in PU learning.

Spatial crowdsourcing is drawing much attention with the rapid development of mobile Internet. Achieving efficient crowdsourcing task assignment involves not only maximizing the earnings of workers but also balancing the preferences of users or customers. Users often express preferences for specific workers or conditions, such as particular drivers, delivery personnel, or service providers. To address this challenge, we investigate the future-aware balanced preference (FABP) problem. This problem aims to maximize the profits of global workers while simultaneously considering the preferences of both parties to ensure bilateral satisfaction. To address the FABP problem, we propose the learning to match (LTM) algorithm. This algorithm utilizes online reinforcement learning that considers both immediate profits and long-term rewards. It acknowledges the significance of task assignment decisions in relation to the spatial distribution of future drivers, which in turn affects subsequent decisions. The LTM algorithm generates future-aware preference lists using learned driver state values and guides the subsequent matching. Additionally, we present the real-time preference-based matching (RTPM) algorithm, which is a real-time matching algorithm that enables substitutions based on preference lists when a more preferred matching pair becomes available. This enhances the efficiency and fairness of real-time task assignment in dynamic environments, while simultaneously meeting the needs of passengers and drivers. Our extensive experiments on both real and synthetic datasets validate the effectiveness of our proposed algorithms, demonstrating a noteworthy improvement of up to 11.8% and an average increase of 4.7% compared to benchmark algorithms.

As a fundamental tool for sequential decision-making, the Combinatorial Multi-Armed Bandits model (CMAB) has been extensively analyzed and applied in various online applications. However, the privacy concerns in budgeted CMAB are rarely investigated thus far. Few bandit algorithms have adequately addressed the privacy-preserving budgeted CMAB setting. Motivated by this, we study this setting using differential privacy as the formal measure of privacy. In this setting, playing an arm yields both a random reward and a random cost, and these values are kept private. In addition, multiple arms can be played in each round. The objective of the decision-maker is to minimize regret while subject to a budget constraint on the cumulative cost of all played arms. We demonstrate an exploration-exploitation-balanced bandit policy, which preserves the privacy of both rewards and costs under budgeted CMAB settings. This policy is proven differentially private and achieves an upper bound on regret. Furthermore, to provide incentives for the differentially private bandit policy so as to ensure that the reported costs are truthful, we introduce the concept of truthfulness and incorporate a payment mechanism that has been proven to be$\sigma$-truthful. Numerical simulations based on multiple real-world datasets validate the theoretical findings and demonstrate the effectiveness of our policy compared to state-of-the-art policies.

In recommender systems, it is frequently presumed that missing ratings adhere to a missing at random (MAR) mechanism, implying the absence of ratings is independent of their potential values. However, this assumption fails to hold in real-world scenarios, where users are inclined to rate items they either strongly favor or disfavor, introducing a missing not at random (MNAR) scenario. To tackle this issue, prior researchers have utilized explicit MAR feedbacks to infer the propensities of unobserved, implicit MNAR feedbacks. Nonetheless, acquiring explicit MAR feedbacks is resource-intensive and time-consuming and may not reflect users’ true preferences. Furthermore, most methods have only been tested on synthetic or small-scale datasets, thus their applicability and effectiveness in real-world settings without MAR feedbacks remain unclear. Along these lines, we aim to predict MNAR ratings without MAR prior propensities by exploring the consistency between MAR and MNAR feedbacks and narrowing the gap between them. From the empirical study and preliminary experiment, we hypothesize thatuser preferencescan be treated as the common prior propensity for both MAR and MNAR generative processes. In this way, we extend this hypothesis to a more general MNAR scenario: user preferences learned from MNAR can partially substitute for the prior propensities derived from MAR feedbacks for MNAR recommendation tasks. To validate our hypothesis and approach, we develop a lightweight iterative probabilistic matrix factorization framework (lightIPMF) as a practical method of our methodology, utilizing user preferences extracted from MNAR, not MAR, to estimate MNAR feedbacks. Finally, the experimental results show that modeling user preferences can effectively improve MNAR feedback estimation without MAR feedback, and our proposed lightIPMF outperforms the state-of-the-art MNAR methods in predicting MNAR feedbacks.

Micro-mobility has become a vital means of transportation in recent years, however, it has also resulted in a rise in traffic incidents. Timely tracking and predicting riders' maneuvers hold the potential to ensure active protection and allow for sufficient time to avert accidents by issuing timely warnings and interventions. We contend that the rider's head dynamics can provide valuable information regarding their subsequent maneuvers. Riders' traveling habits, however diverse, not to mention the rapidly varying riding environment. The above factors contribute to significant disruptions in the data source, and various micro-mobility forms further exacerbate the issue. We accordingly present HeadMon+, which predicts the rider's subsequent maneuver by examining their head dynamics, and it can effectively adapt to various riding conditions and individuals. The system incorporates a deep learning framework with an advanced domain adversarial network. By single-time pre-training, HeadMon+ is capable of adapting to new data domains, including human subjects, and riding conditions for robust maneuver prediction. Based on our evaluation, we have found that the maneuver prediction of HeadMon+ has an overall precision of 94% with a prediction time gap of 4 seconds. HeadMon+'s low cost and rapid response capability make it easily deployed and then contribute to enhancing safe riding.

Mobile CrowdSensing (MCS) has emerged as a popular paradigm to engage mobile users in collaborative sensing tasks. However, its performance is hindered by its limited spatiotemporal range and the cost of data collection. An effective strategy is to integrate Sparse MCS with data completion, allowing for unsensed data inference. However, when confronted with situations where sensed data is excessively sparse, data inference results may be unsatisfactory due to several challenges including: 1) uneven data distribution, 2) complex spatiotemporal correlation, and 3) the presence of inference noise. To address these challenges, we propose a model named Spatiotemporal Evolutionary Inference (STEI) that achieves accurate inference of unsensed data in Sparse MCS. Specifically, we complete the unsensed data by uncovering strong local correlations in the data and gradually evolving those correlations to the global situation. In each evolution step, we thoroughly consider the impact of spatiotemporal consistency and difference. To minimize the interference of noise during the evolution process, we design an adaptive coefficient to enhance the dependence on sensed data. Finally, to validate the effectiveness of STEI, we conduct extensive qualitative and quantitative experiments using three popular datasets. The experimental results demonstrate that our approach excels in accurately inferring data, particularly in situations where the distribution of data is notably uneven.

Mobilecrowdsensing (MCS) is a powerful technique that enables a variety of urban tasks, including temperature monitoring, location-based services, and urban path recommendations. However, these tasks often face the challenge of sparse and incomplete sensing data, undermining their effectiveness and reliability.Sparsedatacompletion (SDC) methods have been developed to infer missing or unobserved data by leveraging spatio-temporal correlations to tackle this issue. This forms the core concept of thesparsemobilecrowdsensing problem (SMCS), which aims to improve the performance of downstream tasks through inferred data. Despite the potential benefits, most existing SMCS methods fail to consider the trade-off between the cost of SDC and the benefits for downstream tasks. These methods often treat SDC and downstream tasks as independent modules, resulting in suboptimal outcomes. In this paper, we investigate the impact of SDC on the SMCS paradigm, both qualitatively and quantitatively. We establish the upper bound of performance achievable when applying SDC in SMCS under different levels of sensing data sparsity. Based on these studies and findings, we propose a practical and flexible framework calledSDC-EVA,SensingDataCompletionEVAluation framework. This framework allows for applying different SDC methods in SMCS, considering factors such as computing complexity, storage space, and associated costs. Our proposed framework allows researchers to assess the necessity and feasibility of integrating SDC into SMCS systems before designing and deploying them in real-world scenarios. This assessment can be tailored to specific data sparsity and contextual information. To validate the effectiveness of our proposed evaluation framework, we conduct experiments in various real-world scenarios involving different combinations of SDC and downstream tasks. The results demonstrate the superiority of our framework in improving the performance of SMCS. By presenting these findings, we aim to contribute to developing SMCS techniques and provide valuable insights for researchers and practitioners.

Although micro-mobility has become a popular and indispensable mode of transportation in recent years, it has also introduced a large number of traffic accidents. Timely tracking and predicting the maneuvers hold the potential to prevent accidents through prompt warnings and interventions. However, the open and simple structure of micro-mobility makes it hard to install sophisticated infrastructures for maneuver prediction. In this paper, we argue that the micro-mobility body dynamics provide sufficient information for maneuver prediction. Our preliminary study suggests that micro-mobility body dynamic patterns appear beforehand and exhibit the correlation with steering maneuvers. We accordingly present RideGuard, which leverages a built-in Inertial Measurement Unit on smartphones to achieve the prediction of steering maneuvers. Through a dual-stream CNN deep learning architecture, RideGuard effectively captures complex patterns and feature relationships from the time and frequency domain. Our extensive real-traffic experiments involving 20 participants demonstrate the superiority of RideGuard: employing a 3s detection window, RideGuard attains a minimum of 94% precision in maneuver prediction with a 5s prediction time gap. The low-cost and rapid response feature of RideGuard enables feasible deployment and promotes safer riding practices. Additionally, we open-source our well-labeled dataset to facilitate further research.

Mobile crowdsensing (MCS) is a paradigm for data collection with the limitation of budgets and worker availability. The central strategy of MCS is recruiting workers to sense a part of data and subsequently infer the unsensed data. To infer unsensed data, prior research has proposed several algorithms that do not require historical data, but their inference accuracy is very limited. More effective works are training a model with sufficient historical data. However, such methods can’t infer data with few to none historical data. A more promising strategy is training models from other similar datasets that have been sensed. However, such datasets are different in terms of sensing locations, numbers of sensed data and data types. Such variance introduces the complex issue of integrating knowledge from these datasets and then training inference models. To solve these, we propose a meta-knowledge based multiple data inference framework named META-MCS. In META-MCS, we propose a similarity evaluation model TMFS. Following this, we cluster similar datasets and train generalized models for each cluster. Finally, META-MCS selects an appropriate model to infer unsensed data. We validate our proposed methods through extensive experiments using ten different datasets, which substantiate the effectiveness of our framework.

Mobile Crowdsensing is a type of technology that utilizes mobile devices and volunteers to gather data about specific topics at large scales in real-time. However, in practice, limited participation leads to missing data, i.e., the collected data may be sparse, which makes it difficult to perform accurate analysis. A possible technique called sparse crowdsensing incorporates the sparse case with data completion, where unsensed data could be estimated through inference. However, sparse crowdsensing typically suffers from poor performance during the data completion stage due to various challenges: the sparsity of the sensed data, reliance on numerous timeslots, and uncertain spatiotemporal connections. To resolve such few-shot issues, the proposed solution uses the Correlated Data Fusion for Matrix Completion (CDFMC) approach, which leverages a small amount of objective data to retrain an auxiliary dataset-based pre-trained model that can estimate unsensed data efficiently. CDFMC is trained using a combination of the traditional Deep Matrix Factorization and the Kalman Filtering, which not only enables the efficient representation and comparison of data samples but also fuses the objective data and auxiliary data effectively. Evaluation results show that the proposed CDFMC outperforms baseline techniques, achieving high accuracy in completing unsensed data with minimal training data.

Decentralized federated learning (DFL) overcomes the single point of failure issue of centralized federated learning. Building upon DFL, blockchain-based federated learning (BFL) takes further strides in establishing trust, enhancing security, and fault tolerance. However, BFL based on the classical linear blockchain exhibits diminished training efficiency in heterogeneous environments and is limited by the performance bottleneck of blockchain. Recent solutions introduce the directed acyclic graph (DAG) blockchain to address these issues, yet they compromise the verifiability of BFL, struggle with handling outdated models, and have a slow convergence speed. In this paper, we propose TGFL, a decentralized federated learning framework based on the Tree-Graph blockchain. The underlying blockchain structure of TGFL is designed as a block-centered DAG to support verifiable and semi-asynchronous training. To facilitate fast convergence, we design a pivot chain generation algorithm that topologically sorts the semi-asynchronous training process, guiding participants in sampling appropriate models. The consensus mechanism, which is closely integrated with federated learning, ensures that the TGFL can effectively resist attacks on the model and the blockchain system. Extensive experiments in various settings demonstrate that TGFL can achieve better training efficiency and model accuracy compared to three baselines.

The past flourishing years of sequential location-based services began with the introduction of the Self-Attention Network (SAN), which quickly superseded CNN or RNN as the state-of-the-art backbone. Recent works utilize modified attention mechanisms or neural network layers to process spatial-temporal factors to realize fine-grained individual behavior pattern modeling. However, we argue these methods can be further improved due to the significant increase in the model's parameter scale or computational burden. In this paper, we first exploit two lightweight approaches, Rotary Time Aware Position Encoder (RoTAPE) and multi-head Interval Aware Attention Block (IAAB), to impel SAN by efficiently and effectively capturing spatial-temporal intervals among the user's visited locations, which require neither extra parameters nor a high computational cost. On the one hand, RoTAPE encodes the day- and hour-level timestamps into sequence representation simultaneously via a sinusoidal encoding matrix, and the corresponding time intervals can be explicitly captured by SAN. Specifically, the multi-level temporal differences are mutually independent to reflect the periodical pattern and jointly complete to measure the absolute time interval. On the other hand, IAAB, point- wise injecting the historical spatial-temporal intervals into the attention map, can promote SAN attaching importance to the spatial relations under the constraints of time conditions. Then, we design a novel MLP-based module, Spatial-Temporal Relation Memory (STR Memory), implemented with fully connected linear layers and matrix transpose operations. STR Memory, endowing the interactions inside historical intervals along different directions, can convert the historical intervals into spatial-temporal relations in future trajectories for accurate predictions. To this end, we propose an end-to-end mobility trajectory prediction framework, namely STiSAN$^+$, employing RoTAPE, stacking multiple layers of IAAB-based encoder-decoder architecture, and coupling with STR Memory. We conducted numerous experiments on six public LBSN datasets to evaluate our proposed algorithm. From Next Location Recommendation to Multi-location Future Trajectory Prediction, our STiSAN$^+$gains average 15.05% and 18.35% improvements against several state-of-the-art sequential models, respectively. Ablation studies demonstrate the effectiveness of RoTAPE, IAAB, and STR Memory under our framework. Moreover, we separately validate the extensibility and interpretability of RoTAPE and IAAB through non-sampled metric evaluation and visualization.

We study defending strategies against adversarial attacks onCombinatorial Multi-Armed Bandits(CMAB) algorithms. CMAB is an effective sequence decision making tool that has been broadly applied in online real-world applications. We consider a realistic CMAB setting, budgeted CMAB, in which multiple arms associated with pulling costs and unknown rewards are pulled per round, aiming to maximize the cumulative reward under a budget constraint. However, the adversarial attack against budgeted CMAB is rarely studied, posing a very important security issue. Specifically, a suboptimal arm that is not pulled (i.e., attacker) can hijack the budgeted CMAB algorithm's behavior, forcing itself to be pulled frequently by manipulating other arms' rewards. Existing strategies cannot prevent such attacks. Motivated by this, we closely study the adversarial attack against a popular budgeted CMAB algorithm, exposing a significant security threat to real-world applications. The attack extends to other algorithms with certain customization. To address this, we incorporate a truthful pricing-based defending strategy that prevents such attacks effectively and ensures arms share pulling costs truthfully. Extensive simulations have illustrated the proposed attack strategy can hijack the algorithm efficiently, while the defending strategy provides attack prevention, individual rationality, and asymptotic truthfulness guarantees.

Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them bored and unsatisfied. The key to solving these issues is learning users' fine-grained preferences and recommending appealing and unexplored items deviating from users' historical items. However, existing models consider cold-start or filter bubble problems separately and ignore that they can reinforce mutually and damage the models' performance accuracy. To this end, we devise a novel serendipity-oriented recommender system (GenerativeSelf-constrainedSerendipitousRecommenderSystem,GS$^{2}$-RS) that generates users' fine-grained preferences to enhance the recommendation performance. Specifically, GS$^{2}$-RS extracts users' interest and satisfaction preferences and generates virtual but convincible neighbors' preferences from themselves with a twin Conditional Generative Adversarial Nets (not from real neighbors). Then we introduce the serendipity item, which is low-interest but high-satisfaction among candidate items. We use the serendipity item to improve the diversity of recommended items, which relieves the filter-bubble problem. Along with this line, a gated mechanism is applied to their fine-grained preferences (interests, satisfactions) to obtain their serendipity items. Finally, these serendipity items are inversely injected into the original user-item rating matrix and build a relatively dense matrix as the input for backbone RS models. Note that GS$^{2}$-RS tackles cold-start and filter-bubble problems in a unified framework without any additional side information and enriches the interpretability of recommendation models. We comprehensively validate GS$^{2}$-RS for solving cold-start and filter bubble problems on four real-world benchmark datasets. Extensive experiments illustrate GS$^{2}$-RS's superiority in accuracy, serendipity, and interpretability over state-of-the-art models. Also, we can plug our model into existing recommender systems as a preprocessing procedure to enhance their performance.

Parking violation is a common urban problem in major cities all over the world. Traditional approaches for detecting parking violations mainly rely on fixed deployed sensors and enforcement agencies, which suffer from high deployment costs and limited coverage. With the rapid development of mobile networks, Mobile CrowdSensing (MCS) has been an effective sensing paradigm. The crowdsensing data can help predict the future parking violation distribution, and the prediction results can provide guidance for user scheduling, i.e., sending the mobile users to patrol areas where many parking violation events may occur. Inspired by this idea, we propose a comprehensive data-driven crowdsensing framework, which incorporates the nested design of a generative model for spatial-temporal data and a user scheduling model. The generative model extracts parking violation hotspots via a data completion module and violation prediction module. Since crowdsensing data is usually temporally sparse and unevenly distributed, a data completion module is proposed to infer the missing statistics in unsensed areas. The violation prediction module then predicts the parking violation distribution. Given the predicted results, the deep reinforcement learning-based user scheduling model coordinates users to visit hotspots for violation detection. Iteratively, the newly collected data can be used to predict the future violation distribution. Finally, we conduct extensive simulations based on two real-world datasets from two large urban cities. The simulation verifies the prediction accuracy and scheduling effectiveness of the proposed framework compared with the baselines.

Mobile CrowdSensing (MCS) is a promising sensing paradigm that leverages users’ mobile devices to collect and share data for various applications. A key challenge in MCS is task allocation, which aims to assign sensing tasks to suitable users efficiently and effectively. Existing task allocation approaches are mostly centralized, requiring users to disclose their private information and facing high computational complexity. Moreover, centralized approaches may not satisfy users’ preferences or incentives. To address these issues, we propose a novel distributed task allocation scheme based on route navigation systems. We consider two scenarios: time-tolerant tasks and time-sensitive tasks, and formulate them as potential games. We design distributed algorithms to achieve Nash equilibria while considering users’ individual preferences and the platform’s task allocation objectives. We also analyze the convergence and performance of our algorithm theoretically. In the time-sensitive task scenario, the problem becomes even more intricate due to temporal conflicts among tasks. We prove the task selection problem is NP-hard and propose a distributed task selection algorithm. In contrast to existing distributed approaches that require users to deviate from their regular routes, our method ensures task completion while minimizing disruption to users. Trace-based simulation results validate that the proposed algorithm attains a Nash equilibrium and offers a total user profit performance closely aligned with that of the optimal solution.

Mobile CrowdSensing (MCS) is a widely adopted approach that involves engaging mobile users to collaboratively perform diverse sensing tasks. In Sparse CrowdSensing, the completion of data from partially-sensed sources plays a pivotal role in urban sensing applications. This process is essential as it enables efficient data representation, enhances urban analysis capabilities, and ultimately facilitates informed city planning decisions. By leveraging the power of mobile users, Sparse CrowdSensing contributes to the comprehensive understanding of urban environments, enabling effective utilization of data for optimizing urban infrastructure and fostering sustainable urban development. To achieve accurate completion results, previous methods usually utilize the universal similarity and conventional tendency while incorporating only a single dataset to infer the full map. However, in real-world scenarios, there may exist many kinds of data (inter-data), that could help to complement each other. Moreover, for each kind of data (intra-data), there usually exists a few but important spatiotemporal fracture data which caused by the special events (e.g. data loss, equipment failure, etc.), which may behave in a different way as the statistical patterns. Thus, it is an essential task to consider spatiotemporal fracture data caused by the special cases in spatiotemporal data inference, especially using both intra- and inter-data, because of the following challenges: 1) the sparsity of the sensed data, 2) the complex spatiotemporal relations and 3) the uncertain scale of a spatiotemporal fracture. To this end, focusing on the large-scale spatiotemporal fracture, we propose a data completion method that exploits both intra- and inter-data correlations for enhancing performance. Specifically, for the purpose of generating spatiotemporal fracture data, there isStackedGenerativeMatrixCompletion(SGMC)by combining previous Stacked Deep Matrix Factorization (SDMF) and Generative Adversarial Networks (GANs), which improves a lot. Along this line, we extract the features of spatiotemporal data and further efficiently complete and predict the unsensed data. Finally, we conduct both qualitative and quantitative experiments on two different datasets, and the results demonstrate that the performance of our method outperforms the state-of-the-art baselines.

Sparse Mobile Crowdsensing is an emerging paradigm for data collection with budgets and workers' limitations' which recruits workers to sense a part of spatio-temporal data and infer what is unsensed. In order to achieve high inferring accuracy in all spatio-temporal areas, we need to measure the importance level of each area and sense some important ones. Existing works usually use the average distance or the difficulty level inferred by historical data to measure the area's importance. However, we argue that neither distance nor difficulty level is suitable for measuring the importance. A better approach is inspired by the data itself, i.e., data similarity among different areas. Furthermore, there usually exist multiple data types in sparse mobile crowdsensing, which leads to a more complex inference from two-dimensional data (spatial and temporal) to three-dimensional data (spatial, temporal, and data type). In this paper, we study worker recruitment in a multi-task scenario, which aims to recruit workers to sense important data for a three-dimensional inference. Specifically, we first present the SWDTW method to calculate data similarity, which is used to evaluate data importance. Based on this, we further propose an evaluation model for three-dimensional data similarity and measure the importance of each area. Finally, inspired by generalized greedy and simulated annealing, we propose a worker recruitment method named WRGSA, the target of which is selecting workers to sense important areas to enhance the inference accuracy. Extensive experiments have been conducted over three real-world datasets with multiple data types, which can verify the effectiveness of our proposed methods.

The positioning of wild animals usually requires long-distance, high-precision, low-energy positioning technology, and most of the existing methods use GPS, RFID, etc., either high power consumption, high cost, or small ranging range, low accuracy. This paper comprehensively considers the needs of out-door scene positioning, and combines the characteristics of LoRa technology and drones to solve the problem that it is difficult to achieve the best cost, energy consumption and positioning error in outdoor positioning. A sequential localization algorithm for two scenarios of single / multiple LoRa terminals is proposed. Based on the Cramer-Rao lower bound theory, the Unmanned Air Vehicle(UAV) trajectory is optimized to minimize the LoRa terminal positioning error at extremely low cost and energy consumption. The simulation results show that the proposed method effectively improves the accuracy of positioning LoRa terminals, and provides theoretical guidance for the deployment of single UAV as mobile base station in positioning.

Magnetic shape memory alloy (MSMA) based actuator plays an important role in the field of micro and nano-fabrication due to its large stroke. However, the inherent hysteresis of MSMA seriously affects the application prospects of MSMA-based actuator in the field of precision positioning. In this study, a composite model is proposed for hysteresis in the MSMA-based actuator by coupling an improved fractional-order Bouc-Wen model to a nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model. Based on the proposed model, a neural network adaptive control method is then used to control the MSMA-based actuator, and the controller design takes into account the effect of time delay on the system performance to improve the positioning accuracy of the MSMA-based actuator. First, we fuse the composite NARMAX model with the canonical form of the nonlinear system to describe the MSMA-based actuator. Then, a control strategy is developed to address the impact of the unknown time delay for controller design and achieve the satisfactory positioning accuracy of the MSMA-based actuator. By using Lyapunov theory, the tracking error is proven to be asymptotic convergence. Experimental studies are provided to validate the effectiveness of the proposed modeling and control schemes.

Spatial Crowdsourcing (SC) has been adopted in various applications such as Gigwalk and Uber, where a platform takes location-based tasks (e.g., picking up passengers) from a requester and selects suitable workers to perform them. In most existing works, the platform selects workers based on the requester and worker information, which suffers from serious privacy issues. Some works have considered privacy issues, but they still suffer from either of two limitations: (i) Privacy of the requester and worker cannot be protected simultaneously; (ii) Third-party trusted entities are usually required. Motivated by this, we focus on protecting the privacy of both the requester and the worker without third-party entities while selecting workers. We use randomized response, a widely recognized and prevalent privacy model achieving Local Differential Privacy (LDP), to jointly protect the privacy of workers’ locations and charges based on the location-charge correlation. For the requester, we present a novel mechanism called randomized matrix multiplication to hide the real task locations. More importantly, we prove that the worker selection based on the protected information is non-submodular and NP-hard, which cannot be addressed in polynomial time. To this end, we present an approximate algorithm to solve the problem efficiently, of which the effectiveness is measured by the approximation ratio, i.e., the ratio of the optimal solution to the approximate solution. Finally, simulations based on real-world datasets illustrate that our worker selection outperforms the state-of-the-art method on both privacy protection and worker selection.

Points-of-interest (POIs) recommendation plays a vital role in location-based social networks (LBSNs) by introducing unexplored POIs to consumers and has drawn extensive attention from academia and industry. Existing POI recommender systems usually learn fixed latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatio-temporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers’ complicated check-in actions. To this end, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe the dynamic nature of POIs. Besides, check-in actions to a POI are also affected by the zone where it is located. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance POI embeddings. Along this line, we propose aTime-zone-spacePOI embedding model (ToP), which integrates multi-knowledge graphs and topic model to introduce not only spatio-temporal effects but also sentiment constraints into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in a different period with spatial constraints via knowledge graph learning. To add sentiment constraints, ToP jointly combines these vectors with the zone's representations learned by topic models to make explainable recommendations. ToP considers the time, space, and sentiment of POI in a unified embedding framework, which benefits the POI recommendations. Extensive experiments on real-world Changchun city datasets demonstrate that ToP achieves state-of-the-art performance in terms of common metrics and provides more insights for consumers’ POI check-in actions.

Mobile CrowdSensing (MCS) has recently become a promising data acquisition paradigm, which recruits a large number of users to collect data from the target sensing areas. Obviously, with the increase of sensing scale and the decrease of sensing granularity, traditional MCS cannot fully cover the required sensing areas especially the inaccessible areas. As a variant, Sparse MCS can utilize the spatiotemporal correlations in sensing data to infer the whole sensing map only by sensing a few subareas. However, in many real-world scenarios, such as traffic congestion prediction or parking occupancy detection, inferring the current unsensed data may not be the final goal. By comparison, it is more important to get the future information through the sparse sensed data. In this paper, we turn attention from inferring the current unsensed data to predicting the future unknown data and propose an urban inference and prediction framework in Sparse MCS. To deal with the sparse sensed data, we first present a bipartite-graph-based matrix completion algorithm with spatiotemporal constraints to accurately recover the current full map. Then, by exploiting spatiotemporal correlations based on the inferred full map, we present a Graph Convolutional Networks (GCN) with spatiotemporal attention to predict the future maps. Furthermore, we design a spatiotemporal iterative method to repeatedly update the spatiotemporal attentions and constraints, in order to connect the urban inference and prediction to improve the accuracy of the whole framework. Extensive experiments have been conducted on two types of typical urban sensing tasks with four real-world data sets, which verify the effectiveness of our proposed algorithms in improving the inference and prediction accuracy with the sparse sensed data.

Mobile Crowdsensing (MCS) is a promising paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on selecting a group of users with the best objective ability, e.g., the user's probability or frequency of covering the task locations. However, we argue that the task completion effect depends not only on the user's objective ability, but also on their subjective collaboration likelihood with each other. Furthermore, even though we can find a well-behaved group of users in the single-round scenario, while in the multi-round scenario without enough prior knowledge, we still face the problem of recruiting previously well-behaved user groups (exploitation) or recruiting uncertain user groups (exploration). Additionally, we consider that each user has a different cost, and the platform recruits users under a cost budget; thus, the problem becomes more challenging: users may report fake costs to gain more profits. To address these problems, assuming that the user's information is known, we first convert the single-round user recruitment problem into the min-cut problem and propose a graph theory based algorithm to find the approximate solution. Then, in the multi-round scenario where the user's information is estimated from the previous rounds, to balance the trade-off between exploration and exploitation, we propose the multi-round User Recruitment strategy under the budget constraint based on the combinatorial Multi-armed Bandit model (URMB), which is proven to achieve a tight regret bound. Next, we propose a graph-based payment strategy to achieve truthfulness and individual rationality of users. Finally, extensive experiments on three real-world datasets show that URMB always outperforms the state-of-th-art strategies.

Mobile Crowdsensing (MCS), which assigns outsourced sensing tasks to volunteer workers, has become an appealing paradigm to collaboratively collect data from surrounding environments. However, during actual task implementation, various unpredictable disruptions are usually inevitable, which might cause a task execution failure and thus impair the benefit of MCS systems. Practically, via reactively shifting the pre-determined assignment scheme in real time, it is usually impossible to develop reassignment schemes without a sacrifice of the system performance. Against this background, we turn to an alternative solution, i.e., proactively creating a robust task assignment scheme offline. In this work, we provide the first attempt to investigate an important and realisticRoBustTaskAssignment (RBTA) problem in MCS systems, and try to strengthen the assignment scheme's robustness while minimizing the workers’ traveling detour cost simultaneously. By leveraging the workers’ spatiotemporal mobility, we propose an assignment-graph-based approach. First, an assignment graph is constructed to locally model the assignment relationship between the released MCS tasks and available workers. And then, under the framework of evolutionary multi-tasking, we devise a population-based optimization algorithm, namelyEMTRA, to effectively achieve adequate Pareto-optimal schemes. Comprehensive experiments on two real-world datasets clearly validate the effectiveness and applicability of our proposed approach.

With the increasing prominence of smart mobile devices, an innovative distributed computing paradigm, namely Mobile Crowdsourcing (MCS), has emerged. By directly recruiting skilled workers, MCS exploits the power of the crowd to complete location-dependent tasks. Currently, based on online social networks, a new and complementary worker recruitment mode, i.e., socially aware MCS, has been proposed to effectively enlarge worker pool and enhance task execution quality, by harnessing underlying social relationships. In this paper, we propose and develop a novel worker recruitment game in socially aware MCS, i.e.,Acceptance-awareWorkerRecruitment (AWR). To accommodate MCS task invitation diffusion over social networks, we design a Random Diffusion model, where workers randomly propagate task invitations to social neighbors, and receivers independently make a decision whether to accept or not. Based on the diffusion model, we formulate the AWR game as a combinatorial optimization problem, which strives to search a subset of seed workers to maximize overall task acceptance under a pre-given incentive budget. We prove its NP hardness, and devise a meta-heuristic-based evolutionary approach namedMA-RAWRto balance exploration and exploitation during the search process. Comprehensive experiments using two real-world data sets clearly validate the effectiveness and efficiency of our proposed approach.

We propose LogSummary, an automatic, unsupervised end-to-end log summarization framework for software system maintenance in this work. LogSummary obtains the summarized triples of necessary logs for a given log sequence. It integrates a novel information extraction method that considers semantic information and domain knowledge with a new triple-ranking approach using the global knowledge learned from all logs. Given the lack of a publicly-available gold standard for log summarization, we have manually labeled the summaries of four open-source log datasets and made them publicly available. The evaluation of these datasets and the case studies on real-world logs demonstrate that LogSummary produces highly representative (average ROUGE F1 score of 0.741) summaries efficiently. We have packaged LogSummary into an open-source toolkit and hope it can be a standard baseline and benefit future log summarization works.

Mobile CrowdSensing (MCS) is a popular data collection paradigm which usually faces the problem of sparse sensed data because of the limited sensing cost. In order to address the situation of sparse data, sparse MCS recruits users to sense important areas and infers completed data by data completion, which is crucial in sparse MCS for urban sensing applications (e.g. enhancing data expression, improving urban analysis, guiding city planning, etc.) To achieve accurate completion results, previous methods usually utilize the universal similarity and conventional tendency while incorporating only a single dataset to infer the full map. However, in real-world scenarios, there may exist many kinds of data (inter-data), that could help to complement each other. Moreover, for each kind of data (intra-data), there usually exist a few but important outliers caused by the special events (e.g., parking peak, traffic congestion, or festival parade), which may behave in a different way as the statistical patterns. These outliers cannot be ignored, while it is difficult to detect and recover them in data completion because of the following challenges: 1) the infrequency and unpredictability of outliers’ occurrence, 2) the large deviations against the means compared to normal values, and 3) the complex spatiotemporal relations among inter-data. To this end, focusing on spatiotemporal data with both intra- and inter-data correlations, we propose a matrix completion method that takes outliers’ effects into consideration and exploits both intra- and inter-data correlations for enhancing performance. Specifically, we first conduct the Deep Matrix Factorization (DMF) with multiple auxiliary Neural Networks, which named Stacked Deep Matrix Factorization (SDMF). Note that the loss function of SDMF is no longer the previous MSE loss function, but replaced with an Outlier Value Loss (OVL) function to effectively detect and recover the outliers. Moreover, a spatiotemporal outlier value memory network is added for further enhancing the outlier inference. Finally, we take extensive qualitative and quantitative experiments on two popular datasets each with two types of sensing data, and the experimental results indicate the advantages of our method that outperforms the state-of-the-art methods.

Mobile edge computing (MEC) reduces data service latency by pushing data to the network edge. However, due to the dynamic and diverse requests of mobile users, the problem of mobile edge caching is more complex than cloud caching. Therefore, the existing model-based caching strategies cannot be directly used in the mobile edge caching environment. Besides, when taking the cooperative storage relationship between neighbor edge servers into consideration, the caching problem becomes more difficult. To this end, we formulate an mobile edge caching problem to minimize the total latency in mobile edge computing. Firstly, a heuristic caching strategy is proposed to solve the mobile edge caching problem in the single-time-slot scenario. Then, with the consideration of users’ mobility and the correlation of files, we propose a caching strategy for the multiple-time-slot scenario based on multi-agent deep reinforcement learning. To address the cold start problem in deep reinforcement learning, we adopt the proposed heuristic caching strategy used in the single-time-slot scenario to further optimize the training results. Extensive experiments on generated data and real-world datasets are conducted to verify that the proposed edge caching strategies can achieve the minimum latency compared with the state-of-the-art strategies.

The past flourishing years of sequential point-of-interest (POI) recommendation began with the introduction of Self-Attention Network (SAN), which quickly superseded CNN or RNN as the state-of-the-art backbone. To realize the fine-grained users' behavior patterns modeling, recent works utilize modified attention mechanisms or neural network layers to process spatial-temporal factors. However, due to the significant increase on either model's parameter scale or computational burden, we argue that these methods can be further improved. In this paper, we exploit two lightweight approaches, Time Aware Position Encoder (TAPE) and Interval Aware Attention Block (IAAB), to impel SAN by considering the spatial-temporal intervals among POIs separately, where requiring neither extra parameters nor high computational cost. On the one hand, TAPE, adjusting the positions in sequences based on the timestamps dynamically and generating positional representations with sinusoidal transformation, can enhance sequence representations to reflect both the absolute order and relative temporal proximity among all POIs. On the other hand, IAAB, point-wise adding the scaled spatial-temporal intervals to the attention map, can promote the attention mechanism attaching importance to the spatial relation among all POIs under the constraints of time conditions and providing more explainable recommendation. We integrate these two modules into SAN and propose a Spatial-Temporal Interval-Aware sequential POI recommender, namely STiSAN, as an end-to-end deployment. Experimental results based on three public LBSN datasets and one real-world city transportation dataset demonstrate STiSAN's superior performance (average 13.01% improvement against the strongest baseline). Moreover, we validate the extensibility and interpretability of TAPE and IAAB through metric evaluation and visualization separately.

As a cost-effective paradigm, Sparse Crowdsensing aims to recruit workers to perform a part of sensing tasks and infer the rest, which has broad applications, including environmental monitoring, urban sensing, etc. In most cases, workers will participate in real time, and thus their sensing data are coming dynamically. Taking full advantage of the online coming data to complete the full sensing map is an important problem for Sparse Crowdsensing. However, for data completion, the importance of data collected from different spatio-temporal areas is usually different and time-varying. For example, the newly obtained data in the center is often more important than the old ones from edges. Moreover, the area importance may also influence the following worker selection, i.e., selecting suitable workers to actively sense important areas (instead of passively waiting for given data) for improving completion accuracy. To this end, in this paper, we propose a framework for online Sparse Crowdsensing, called OS-MCS, which consists of three parts: matrix completion, importance estimation, and worker selection. We start from the dynamically coming data and propose an online matrix completion algorithm with spatio-temporal constraints. Based on that, we estimate the spatio-temporal area importance by conducting a reinforcement learning-based up-to-date model. Finally, we utilize the prophet secretary problem to select suitable workers to sense important areas for accurate completion in an online manner. Extensive experiments on real-world data sets show the effectiveness of our proposals.

Recently, the growing popularity of Spatial Crowd-sourcing (SC), allowing untrusted platforms to obtain a great quantity of information about workers and tasks’ locations, has raised numerous privacy concerns. In this paper, we investigate the privacy-preserving task assignment in the online scenario, where workers and tasks arrive at the platform in real time and tasks should be assigned to workers immediately. Traditional online task assignments usually make a benchmark to decide the following task assignment. However, when location privacy is considered, the benchmark does not work anymore. Hence, how to assign tasks in real time based on workers and tasks’ obfuscated locations is a challenging problem. Especially when many tasks could be assigned to one worker, path planning should be considered, making the assignment more challenging. To this end, we propose a Planar Laplace distribution based Privacy mechanism (PLP) to obfuscate real locations of workers and tasks, where the obfuscation does not change the ranking of these locations’ relative distances. Furthermore, we design a Threshold-based Online task Assignment mechanism (TOA), which could deal with the one-worker-many-tasks assignment and achieve a satisfactory competitive ratio. Simulations based on two real-world datasets show that the proposed algorithm consistently outperforms the state-of-the-art approach.

While Mobile CrowdSensing (MCS) has become a popular paradigm that recruits mobile users to carry out various sensing tasks collaboratively, the performance of MCS is frequently degraded due to the limited spatiotemporal coverage in data collection. A possible way here is to incorporate sparse MCS with data inference, where unsensed data could be completed through prediction. However, the spatiotemporal data inference is usually "fractured" with poor performance, because of following challenges: 1) the sparsity of the sensed data, 2) the unpredictability of a spatiotemporal fracture and 3) the complex spatiotemporal relations. To resolve such fracture data issues, we elaborate a data generative model for achieving spatiotemporal fracture data inference in sparse MCS. Specifically, an algorithm named Generative High-Fidelity Matrix Completion (GHFMC) is proposed through combining traditional Deep Matrix Factorization (DMF) and Generative Adversarial Networks (GAN) for generating spatiotemporal fracture data. Along this line, GHFMC learns to extract the features of spatiotemporal data and further efficiently complete and predict the unsensed data by using Binary Cross Entropy (BCE) loss. Finally, we conduct experiments on three popular datasets. The experimental results show that our approach performs higher than the state-of-the-art (SOTA) baselines in both data inference accuracy and fidelity.

Propagation delay and channel loss are two vital factors affecting reliability of Underwater Acoustic Networks (UANs). Different from land networks, UANs have long prop-agation delay and poor channel quality, which lead to serious data collision and high bit error rate, respectively. However, complex underwater environments impose great challenges to evaluate propagation delay and channel loss. As temperature is the most critical factor affecting them, in this paper, we propose to employ temperature to evaluate them. However, existing temperature prediction research are insufficient for accuracy or efficiency. This paper proposes a temperature prediction-assisted approach for evaluating propagation delay and channel loss, aiming to improve reliability and performance of underwater acoustic networks. We build a nonlinear autoregressive dynamic neural network-based temperature prediction model to improve prediction accuracy and reduce time complexity. Then, we evaluate propagation delay and channel loss considering different marine environments, including shallow and deep sea. Extensive simulation results show that our approach performs better than five advanced baselines.

The prosperity of industrialization urges modern ride-sharing platforms to gain profit from efficient management of their resources. Although ride-sharing allows sharing costs and promotes the traffic efficiency by making better use of vehicle capacities, dealing with large amounts of online taxi orders is an inevitable challenge in the current transportation systems, where all drivers have to make immediate and irrevocable decisions about whether to accept current order in a parallel way. Furthermore, in order to achieve global fairness, it is critical for an algorithm to function whenever the first order gets on-line without any observation stage. In this paper, we formulate this online user selection problem as a prophet inequality for independent identically distributed random variables from an unknown distribution. We construct a sample set to avoid the observation stage in an online decision process. Considering the driver-centered ride-sharing scenario, a route schedule algorithm and a sample-driven algorithm with a guarantee of lower bound are proposed to concurrently guide taxi drivers to accept taxi orders and achieve global fairness at the meantime. Finally, we conduct extensive evaluations based on three real-world data sets. The results verify the effectiveness of our proposed algorithm on improving the overall profit, increasing accepted orders and reducing the unoccupied time of the vehicle under the valid ride-sharing constraints.

With the development of communications, networking, and information technology, Crowdsensed Data Trading (CDT) becomes a novel data trading paradigm. In CDT, the data requesters publish crowdsensing tasks with specific data requirements, and then workers complete these tasks, upload the data and obtain corresponding rewards. To efficiently deal with data trading, most of the existing CDT systems assume a trusted centralized platform. However, we argue that the platform may collude with workers or requesters to trick others for achieving more benefits. For example, according to the workers’ uploaded data, the platform can modify the reward functions by colluding with the requester. Similarly, the platform might collude with workers to let them know the reward function, then workers could forge data. Meanwhile, requesters and workers may also be malicious. For example, requesters may post tasks but fail to pay and workers can upload wrong data to mislead the system. To solve the above problems, we combine the Crowdsensed Data Trading system with intelligent Blockchain (CDT-B), which contains a smart contract called CDToken. As a credible third-party, the CDToken is used to record the requesters’ reward function and workers’ data uploading function to avoid targeted trick. At the same time, we not only design a Data Uploading and Preprocessing (DUP) mechanism in CDToken to collect and process the workers’ sensed data, but also propose a Grouping Truth Discovery (GTD) to evaluate their data quality for determining the payments. Moreover, to hold a large number of requesters and workers in CDT-B, we propose a Layered Sharding blockchain based on Membership Degree (LSMD) to solve the blockchain inefficiency problem. Finally, we deploy CDToken to an experimental environment based on Ethereum and demonstrate its efficient performance and practicability.

With the boosting of neural networks, recommendation methods become significantly improved by their powerful ability of prediction and inference. Existing neural-network based recommender systems (NN-RSs) usually first employ matrix embedding (ME) as a pre-process to learn users’ and items’ representations (latent vectors), then input these representations to a specific modified neural network framework to make accurate Top-k recommendations. Obviously, the performance of ME has a significant effect on RS models. However, most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between users and items (e.g., users who rate the same ratings for the same items should be embedded into similar representations), which is an ideological disadvantage. On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage. In this paper, we propose a supervised collaborative representation learning model - Magnetic Metric Learning (MML) - to map users and items into a unified latent vector space, enhancing the representation learning for NN-RSs. First, MML utilizes dual triplets to model not only the observed relationships between users and items, but also the underlying relationships between users as well as items to overcome the ideological disadvantage. Specifically, a modified metric-based dual loss function is proposed in MML to gather similar entities and disperse the dissimilar ones. With MML, we can easily compare all the relationships (user to user, item to item, user to item) according to the weighted metric, which overcomes the methodological disadvantage. We conduct extensive experiments on four real-world datasets with large item space. The results demonstrate that MML can learn a proper unified latent space for representations from the user-item matrix with high accuracy and effectiveness, and lead to a performance gain over the state-of-the-art RS models by an average of 17 percent.

In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose to threaten the retailers for illegal profits. PMUs are difficult to be detected because they utilize masking strategies to disguise themselves as normal users. Specifically, there are three challenges for PMU detection: 1) professional malicious users do not conduct any abnormal or illegal interactions (they never concurrently leave too many negative reviews and low ratings at the same time), and they conduct masking strategies to disguise themselves. Therefore, conventional outlier detection methods are confused by their masking strategies. 2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem. 3) there are no datasets with labels for professional malicious users in public, which makes PMU detection an unsupervised learning problem. To this end, we propose an unsupervised multi-modal learning model: MMD, which employs Metric learning for professional Malicious users Detection with both ratings and reviews. MMD first utilizes a modified RNN to project the informational review into a sentiment score, which jointly considers the ratings and reviews. Then professional malicious user profiling (MUP) is proposed to catch the sentiment gap between sentiment scores and ratings. MUP filters the users and builds a candidate PMU set. We apply a metric learning-based clustering to learn a proper metric matrix for PMU detection. Finally, we can utilize this metric and labeled users to detect PMUs. Specifically, we apply the attention mechanism in metric learning to improve the model’s performance. The extensive experiments in four datasets demonstrate that our proposed method can solve this unsupervised detection problem. Moreover, the performance of the state-of-the-art recommender models is enhanced by taking MMD as a preprocessing stage.

Mobile crowdsensing (MCS) has attained much attention for gathering distributed mobile users to complete large-scale sensing tasks. To ensure the task completion, enough users should be motivated to participate in sensing tasks. Thus, much research in MCS focuses on proposing incentive mechanism, and these works usually focus on the offline scenario, where the information of all users is available to the platform. However, we argue that the actual MCS is usually an online scenario, where the platform does not know the user's information until they establish connections with the platform. Meanwhile, mobile users connect to the platform randomly and will cut off the connection at any time. Hence, when accessed by a user, the platform needs to make an irrevocable decision instantly about whether to select the user or not, and decides a remuneration for the user without knowing future information. In this paper, we first propose a reverse-auction framework to model the interaction between the platform and mobile users. Then, we present an online truthful incentive mechanism (OTIM) to motivate users, including online winner selection and remuneration determination strategies. Finally, massive simulations are conducted based on three real traces, and the simulation results illustrate that OTIM achieves truthfulness, individual rationality, computational efficiency and an approximately full budget utilization.

Vehicular CrowdSensing (VCS) has become a powerful sensing paradigm by selecting users driving vehicles to perform tasks. In most existing research, the platform centrally allocates tasks according to the collected user information. We argue that the information collection process results in user privacy leakage, and the centralized allocation leads to a heavy computation complexity. We propose to apply a distributed task allocation method in the widely-used route navigation system. The navigation system recommends several routes to a user and each route may cover some tasks. Then, the user distributively selects a route according to the route profit (task reward minus route cost). Since the task reward is shared by users, the route selections of users may influence each other. Hence, it remains unclear how to design a distributed route navigation approach to reach an equilibrium state (i.e., each user is satisfied with the selected route), while guaranteeing a good total profit. To this end, we formulate the problem as a multi-user potential game and propose a distributed route navigation algorithm. The trace-based simulation results verify that the proposed algorithm achieves a Nash equilibrium, while achieving a total user profit performance close to that of the optimal solution.

Points-of-interest (POIs) recommendation plays a vital role by introducing unexplored POIs to consumers and has drawn extensive attention from both academia and industry. Existing POI recommender systems usually learn latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatiotemporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers complicated check-in actions. In this paper, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe POIs dynamic function. Besides, check-in actions to a POI is also affected by the zone it belongs to. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance the accuracy of POI recommendations. Along this line, we propose a Time-dependent Zone-enhanced POI embedding model (ToP), a recommender system that integrates knowledge graph and topic model to introduce the spatiotemporal effects into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in different time to capture its dynamic functions. Jointly combining these vectors with zones representations, ToP enhances the spatiotemporal interpretability of POI recommendations. With this hybrid architecture, some existing POI recommender systems can be treated as special cases of ToP. Extensive experiments on real-world Changchun city datasets demonstrate that ToP not only achieves state-of-the-art performance in terms of common metrics, but also provides more insights for consumers POI check-in actions.

Sparse spatiotemporal data completion is crucial in Mobile CrowdSensing for urban application scenarios. In fact, accurate urban data completion can enhance data expression, improve urban analysis, and ultimately guide city planning. However, it is a non-trivial task to consider outlier values caused by the special events (e.g., parking peak, traffic congestion, or festival parade) in spatiotemporal data completion because of the following challenges: 1) the rarity and unpredictability, 2) the inconsistency compared to normal values, and 3) the complex spatiotemporal relations. In spite of the considerable improvements, recent deep learning-based methods overlook the existence of outlier values, which results in misidentifying these values. To this end, focusing on spatiotemporal data, we propose a matrix completion method that takes outlier value effects into consideration. Specifically, an outlier value model is proposed by adding a memory network and modifying the loss function to traditional matrix completion. Along this line, we extract the features of outlier values and further efficiently complete and predict the unsensed data. Finally, we conduct both qualitative and quantitative experiments on three different datasets, and the results demonstrate that the performance of our method outperforms the state-of-the-art baselines.

Mobile Edge Computing (MEC) has been envisioned as a promising distributed computing paradigm, where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation. However, most existing works only consider the congestion of wireless channels as the crucial factor influencing the strategy-making process, and ignore the impact of the offloading among edge nodes. In addition, centralized task offloading strategies result in heavy computation complexity in center nodes. Along this line, we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users’ offloading strategies. To this end, we first formulate the offloading problem as a multi-user potential game, and then propose a distributed task offloading algorithm to reach an equilibrium state which can also protect individual privacy. Specifically, in the above task offloading algorithm, we propose two subalgorithms to select users for updating strategies: Parallel User Selection Algorithm (PUS) and Single User Selection Algorithm (SUS) in order to substantially accelerate the convergence. Extensive experiments on three real-world data sets validate that the proposed algorithm achieves a Nash equilibrium and effectively decreases the total user cost which is acceptable compared to the optimal solution.

Most existing research about task allocation in mobile crowdsensing mainly focus on requester-centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker-centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks.

With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach.

Mobile crowdsensing (MCS) has gained much attention due to the proliferation of smart devices equipped with powerful sensors. Large-scale users are the foundation of MCS, so designing incentive mechanisms to motivate users to participate in MCS is necessary. Existing works on incentive mechanisms usually assume a scenario where a group of tasks arrive at the platform at the same time and are immediately assigned to users. We argue that a more realistic MCS scenario can delay a task, which is called the assignment duration time, to wait for appropriate users. In this scenario, we focus on proposing a truthful incentive mechanism to reduce the overall social cost. Due to the uncertainty of coming users, the problems of selecting the appropriate users and calculating the payment for each recruited user (winner) are more complicated. To overcome these challenges, we design a dynamic truthful incentive mechanism (DTIM) including winner selection and payment decision processes. The former uniformly recruits users before the assignment deadline of tasks and dynamically readjusts the recruiting frequency of other tasks to select winners iteratively, which achieves an approximation ratio. Furthermore, the latter determines truthful payment for each winner to encourage user participation as well as avoid being deceived, which achieves truthfulness, individual rationality, and computational efficiency. Finally, massive simulations based on a real dataset roma/taxi validate the DTIM, which can effectively reduce the overall social cost and make a truthful payment for each winner.

Mobile Crowdsensing (MCS) is a powerful sensing paradigm, which provides sufficient social data for cognitive analytics in industrial sensing, and industrial manufacturing. Considering the sensing costs, sparse MCS, as a variant, only senses the data in a few subareas, and then infers the data of unsensed subareas by the spatio-temporal relationship of the sensed data. Existing works usually assume that the sensed data are linearly spatiotemporal dependent, which cannot work well in real-world nonlinear systems, and thus, result in low data inference accuracy. Moreover, in many cases, users not only require inferring the current data, but also have an interest in predicting the near future, which can provide more information for users' decision making. Facing these problems, we propose a deep learning-enabled industrial sensing, and prediction scheme based on sparse MCS, which consists of two parts: matrix completion and future prediction. Our goal is to achieve high-precision prediction of future moments under the hypothesis of sparse historical data. To make full use of the sparse data for prediction, we first propose a deep matrix factorization method, which can retain the nonlinear temporal-spatial relationship, and perform high-precision matrix completion. In order to predict the subareas' data in several future sensing cycles, we further propose a nonlinear autoregressive neural network, and a stacked denoising autoencoder to obtain the temporal-spatial correlation between the data from different cycles or subareas. According to the results gained by experiments on four real-world industrial sensing datasets consisting of six typical tasks, it can be seen that the method in this article improves the accuracy of prediction using sparse data.

As an effective tool, roadside digital billboard advertising is widely used to attract potential customers (e.g., drivers and passengers passing by the billboards) to obtain commercial profit for the advertiser, i.e., the attracted customers' payment. The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers' implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three real-world datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies.

Mobile crowdsensing has become a significant sensing technique which takes advantage of mobile devices to collect information about the surrounding. The traditional cloud-based centralized mobile crowdsensing architecture generates significant traffic on networks and computation burden on the cloud. In this paper, we investigate the edge-based mobile crowdsensing architecture, where a group of mobile edge servers is deployed at network edge as the bridge between the central server and mobile users for data filtering and aggregation. Each user may collect multiple types of data in mobile crowdsensing. To facilitate data aggregation, the same type of data carried by different users is supposed to be uploaded to the same mobile edge server. In this scenario, a problem emerges: which server should be activated for processing each type of data in order to minimize the total cost? The cost consists of the facility cost (activating server and processing data) and the service cost (the users' movement cost for uploading data). Furthermore, the problem is formulated as a variant of the uncapacitated multi-commodity facility location problem. In particular, two situations of the problem are studied in our work: (1) for the situation where each user carries at most two types of data, we propose a relaxation based approximation algorithm, which is proved to have a bound to the optimal solution; (2) for a more generalized situation where each user can carry multiple types of data, we propose a connected multi-agent simulated annealing algorithm. Finally, we conduct extensive simulations based on the widely-used real-world datasets: roma/taxi, epfl/mobility and geolife trajectory. The simulation results show that the proposed algorithms demonstrate their superiority over baseline methods and are consistent with the theoretical analysis.

Mobile CrowdSensing (MCS) has recently become a powerful paradigm that recruits users to cooperatively perform various tasks. In many realistic settings, users participate in real time and we have to recruit them in an online manner. The existing works usually formulate the online recruitment problem as a budgeted optimal stopping problem with submodular user utility, while we first argue that not only the budget but also the time constraints can jointly influence the recruitment performance. For example, if we have less budget but plenty of time, we should recruit users with more patience. Second, considering the user’s cooperative willingness, its contribution may be diminishing or even irregular. Hence, we also need to address not only submodular cases but also their non-submodular utility. In this paper, we study the online user recruitment problem with (non-)submodular utility under the budget and time constraints. To deal with the two constraints, we first estimate the number of users to be recruited and then recruit them in segments. Moreover, we extend the segmented strategy with a non-submodular utility, which has the submodularity ratio$\gamma $and the competitive ratio$\gamma ^{2}(1-e^{-1})/7$. Furthermore, to correct estimation errors and utilize newly obtained information, we dynamically re-adjust the segmented strategy and also prove that the dynamic strategy achieves a competitive ratio of${\gamma ^{2}(1-e^{-1})(1-e^{-\gamma /2})/7}$. Finally, a reverse auction-based online pricing mechanism is lightly built into the proposed user recruitment strategy, which achieves truthfulness and individual rationality. Extensive experiments on three real-world data sets validate the proposed online user recruitment strategy under the (non-) submodular utility and two constraints.

Logs are one of the most valuable data sources for large-scale service (e.g., social network, search engine) maintenance. Log parsing serves as the the first step towards automated log analysis. However, the current log parsing methods are not adaptive. Without intra-service adaptiveness, log parsing cannot handle software/firmware upgrade because learned templates cannot match new type of logs. In addition, without cross-service adaptiveness, the logs of a new type of service cannot be accurately parsed when this service is newly deployed. We propose LogParse, an adaptive log parsing framework, to support intra-service and cross-service incremental template learning and update. LogParse turns the template generation problem into a word classification problem and learns the features of template words and variable words. We evaluate LogParse on four public production log datasets. The results demonstrate that LogParse supports accurate adaptive template update (increased from 0.559 to nearly 1.0 parsing accuracy), and a trained LogParse is adaptive for a brand new service’s log parsing. Because of LogParse’s adaptiveness, we also apply LogParse to an interesting application, log compression and deployed log compression in a top cloud service provider. We package LogParse into an open-source toolkit.

Mobile CrowdSensing (MCS) is a promising paradigm that recruits mobile users to cooperatively perform various sensing tasks. When assigning tasks to users, most existing works only consider the fairness of users, i.e., the user's processing ability, with the goal of minimizing the assignment cost. However, in this paper, we argue that it is necessary to not only give full use of all the users' ability to process the tasks (e.g., not exceeding the maximum capacity of each user while also not letting any user idle too long), but also satisfy the assignment frequency of all corresponding tasks (e.g., how many times each task should be assigned within the whole system time) to ensure a long-term, double-fair and stable participatory sensing system. Hence, to solve the task assignment problem which aims to reasonably assign tasks to users with limited task processing ability while ensuring the assignment frequency, we first model the two fairness constraints simultaneously by converting them to user processing queues and task virtual queues, respectively. Then, we propose a Fair Task Assignment Strategy (FTAS) utilizing Lyapunov optimization and we provide the proof of the optimality for the proposed assignment strategy to ensure that there is an upper bound to the total assignment cost and queue backlog. Finally, extensive simulations have been conducted over three real-life mobility traces: Changchun/taxi, Epfl/mobility, and Feeder. The simulation results prove that the proposed strategy can achieve a trade-off between the objective of minimizing the cost and the fairness of tasks and users compared with other baseline approaches.

Mobile CrowdSensing (MCS) is a promising paradigm that recruits users to cooperatively perform various sensing tasks. In most realistic scenarios, users dynamically participate in MCS, and hence, we should recruit them in an online manner. In general, we prefer to recruit a user who can make the maximum contribution at the least cost, especially when the recruitment budget is limited. The existing strategies usually formulate the user recruitment as the budgeted optimal stopping problem, while we argue that not only the budget but also the time constraints can greatly influence the recruitment performance. For example, if we have less remaining budget but plenty of time, we should recruit users with more patience. In this paper, we propose a dynamic user recruitment strategy with truthful pricing to address the online recruitment problem under the budget and time constraints. To deal with the two constraints, we first estimate the number of users to be recruited and then recruit them in segments. Furthermore, to correct estimation errors and utilize newly obtained information, we dynamically re-adjust the recruiting strategy and also prove that the proposed strategy achieves a competitive ratio of (1 - 1/e)2/7. Finally, a reverse auction-based online pricing mechanism is lightly built into the proposed user recruitment strategy, which achieves truthfulness and individual rationality. Extensive experiments on three real-world data sets validate the proposed online user recruitment strategy, which can effectively improve the number of completed tasks under the budget and time constraints.

Mobile CrowdSensing (MCS) has recently emerged as a practical paradigm for large-scale and fine-grained urban sensing systems. To reduce sensing cost, Sparse MCS only senses data from a few subareas instead of sensing the full map, while the other unsensed subareas could be inferred by the intradata correlations among the sensed data. In certain applications, users are not only interested in inferring the data of other unsensed subareas in the current sensing cycle, but also interested in predicting the full map data of the near future sensing cycles. However, the intradata correlations exploited from the historical sparse sensed data cannot be effectively used for predicting full data in the temporal-spatial domain. To address this problem, in this paper, we propose an urban prediction scheme via Sparse MCS consisting of the matrix completion and the near-future prediction. To effectively utilize the sparse sensed data for prediction, we first present a bipartite-graph-based matrix completion algorithm with temporal-spatial constraints to accurately recover the unsensed data and preserve the temporal-spatial correlations. Then, for predicting the fine-grained future sensing map, with the historical full sensing data, we further propose a neural-network-based continuous conditional random field, including a Long Short-Term Memory component to learn the non-linear temporal relationships, and a Stacked Denoising Auto-Encoder component to learn the pairwise spatial correlations. Extensive experiments have been conducted on three real-world urban sensing data sets consisting of five typical sensing tasks, which verify the effectiveness of our proposed algorithms in improving the prediction accuracy with the sparse sensed data.

Mobile Crowdsensing (MCS) is a new paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on selecting a group of users with the best objective ability, e.g., the user's probability or frequency of covering the task locations. However, we argue that, for the cooperative MCS task, the completion effect depends not only on the user's objective ability, but also on their subjective collaboration likelihood with each other. In other words, in each single round, we prefer to recruit users with not only a strong objective ability but also good collaboration likelihood. Moreover, even though we could find a well-behaved group of users in a single round, in the multi-round scenario without enough prior knowledge, we still face the problem of recruiting previously well-behaved user groups (exploitation) or recruiting unknown user groups (exploration). To address these problems, in this paper, we first convert the single-round user recruitment problem into the min-cut problem and propose a graph theory based algorithm to find the optimal group of users. Furthermore, in the multi-round scenario, to balance the trade-off between exploration and exploitation, we propose the multi-round User Recruitment strategy based on the combinatorial Multi-armed Bandit model (URMB) and prove that it can achieve a tight regret bound. Finally, extensive experiments on three real-world datasets validate that the users recruited by URMB result in a better task completion effect than the state-of-the-art strategy.

Sparse mobile crowdsensing is a practical paradigm for large sensing systems, which recruits a small number of users to sense data from only a few subareas and, then, infers the data of unsensed subareas. In order to provide high-quality sensing services under a budget constraint, we would like to select the most effective users to collect useful sensing data to achieve the highest inference accuracy. However, due to the variable user mobility and complicated data inference, it is really challenging to directly select the best user set which helps the most with data inference. From the user's side, we can obtain the probabilistic coverage according to the users' mobilities, while the probabilistic coverage cannot indicate the data inference accuracy directly. From the subarea's side, we may identify some more useful subareas under the current states (e.g., the previous sensed subareas and the current expected coverage), while these useful subareas may not be covered by the users. Moreover, both the user mobility and data inference introduce a lot of uncertainty, which yields nonmonotonicity and thus nonsubmodularity in the user recruitment problem. Therefore, in this article, we study the user recruitment problem on both the user's and subarea's sides and propose a three-step strategy, including user selection, subarea selection, and user-subarea-cross (US-cross) selection. We first select some candidate user sets, which may cover the most subareas under the budget constraint (user selection), then estimate which subareas are more useful on data inference according to the selected candidates (subarea selection), which finally guides us to recruit the best user set (US-cross selection). Extensive experiments on two real-world data sets with four types of sensing tasks verify the effectiveness of our proposed user recruitment algorithms, which can effectively enhance the data inference accuracy under a budget constraint.

Mobile crowdsensing recruits a group of mobile users to cooperatively perform a common sensing job with their smart devices. As a special issue, photo crowdsensing allows users to utilize the built-in cameras of mobile devices to take photos for an event or a target. Then, the photos can be used in numerous application areas, such as target reconstruction, scenario reduction, and so on. Therefore, photo crowdsensing has attracted considerable attention recently due to the rich information that can be provided by images. In this paper, we focus on using the photos to make reconstructions for specific targets. Furthermore, we develop a user recruitment system for efficient photo collecting in mobile crowdsensing (RSMC), where the task requesters publish a sensing task to the users, and the map is gridded according to the locations of the sensing targets. Then, we use a semi-Markov model to calculate the user's utility for the sensing task. Finally, a user recruitment strategy is devised to recruit the optimal k users for finishing the sensing task. We conduct extensive simulations based on three widely used real-world traces: roma/taxi, epfl, and geolife. The results show that, compared with other recruitment strategies, RSMC takes the largest number of efficient photos for the sensing task.

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity , which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.

Mobile crowdsensing emerges as a powerful sensing paradigm which utilizes a group of users with mobile devices to perform sensing tasks cooperatively. The traditional mobile crowdsensing architecture is centralized and cloud-based, where the users upload the sensing data directly to the central server. Due to the fact that the amount of sensing data is very large, it will bring much burden to upload and process data collected by mobile users on the central server. In this paper, we propose an edge-based mobile crowdsensing architecture, which introduces a new intermediate layer for data storage, processing and aggregation through deploying mobile edge servers between the traditional cloud server and the user layer. Considering the limited budget, a decision-maker has to decide which server is activated to process each data type at minimum cost, where the cost consists of the facility cost for activating server and processing data, and the service cost measured by the distance of mobile users' movement during the process of uploading data. Since a user may have multiple types of data, this problem is formulated as a variant of the uncapacitated multi-commodity facility location problem. Furthermore, an approximation algorithm is proposed to solve it for minimizing cost, which is proved to have a bound to the optimal solution. We conduct extensive simulations based on the widely-used real-world datasets: roma taxi set, epfl mobility set and geolife trajectory set. The experiment results show that the proposed approximation algorithm outperforms other baseline algorithms and accords with the theoretical bound results.

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogeneous (have diverse spatial-temporal requirements), which make it hard to select an efficient subset of users to perform the tasks. In this paper, we present a point of interest (PoI) based mobility prediction model to obtain the probabilities that tasks would be completed by users. Based on it, we propose a greedy offline algorithm to select a set of users under a participant number constraint. Furthermore, we extend the user selection problem to a more realistic online setting where users come in real time and we decide to select or not immediately. We formulate the problem as a submodular k-secretaries problem and propose an online algorithm. Finally, we design a distributed user selection framework Crowd UserS and implement an Android prototype system as proof of the concept. Extensive simulations have been conducted on three real-life mobile traces and the results prove the efficiency of our proposed framework.

Mobile crowdsensing recruits a massive group of mobile workers to cooperatively finish a sensing task through their smart devices (mobile phones, ipads, etc.). In this paper, the communication in social network for delivering the sensing data of mobile crowdsensing is considered, where some requesters publish the sensing tasks to all the Point of Interests (PoIs), and the workers are recruited to take the sensing data in the PoI until they could communicate with the requester through an offline and online connection. We first use the semi-Markov model to predict the offline encounter situation. Then, the worker's utility is decided by both the offline encounter and social connection probabilities. The Worker Recruitment for Self-organized MSC (WEO) is further presented through recruiting a set of workers, who have the maximum communication probability with the requesters. We prove that the optimal recruitment problem is NP-hard, and we introduce a practical greedy heuristic method for this problem, the performance of the greedy method is also tested. Two real-world traces, roma/taxi and epfl are tested in our simulations, where WEO always achieves the highest delivery ratio of sensing tasks among different recruitment strategies.

Sparse Mobile CrowdSensing (MCS) is a novel MCS paradigm where data inference is incorporated into the MCS process for reducing sensing costs while its quality is guaranteed. Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i.e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i.e., data collection costs) for ensuring a certain level of quality. To address this issue, this paper proposes a Deep Reinforcement learning based Cell selection mechanism for Sparse MCS, called DR-Cell. We properly model the key concepts in reinforcement learning including state, action, and reward, and then propose to use a deep recurrent Q-network for learning the Q-function that can help decide which cell is a better choice under a certain state during cell selection. Experiments on various real-life sensing datasets verify the effectiveness of DR-Cell over the state-of-the-art cell selection mechanisms in Sparse MCS by reducing up to 15% of sensed cells with the same data inference quality guarantee.

In real-world recommendation scenarios, there are two common phenomena: 1) users only provide ratings but there is no review comment. As a result, the historical transaction data available for recommender system are usually unbalanced and sparse; 2) Users' opinions can be better grasped in their reviews than ratings. This indicates that there is always a bias between ratings and reviews. Therefore, it is important that users' ratings and reviews should be mutually reinforced to grasp the users' true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation (NeuO). Specifically, we exploit a two-step training neural networks, which utilize both reviews and ratings to grasp users' true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring method (SC), which employs dual attention vectors to predict the users' sentiment scores of their reviews. A combination function is designed to use the results of SC and user-item rating matrix to catch the opinion bias. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user-item matrix. Extensive experiments on real-world data demonstrate that our approach can achieve a superior performance over state-of-the-art baselines on real-world datasets.

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job. One of the users' main concerns is the cost of data uploading, which affects their willingness to participate in a crowdsensing task. In this paper, we propose an efficient Prediction-based User Recruitment for mobile crowdsEnsing (PURE), which separates the users into two groups corresponding to different price plans: Pay as you go (PAYG) and Pay monthly (PAYM). By regarding the PAYM users as destinations, the minimizing cost problem goes to recruiting the users that have the largest contact probability with a destination. We first propose a semi-Markov model to determine the probability distribution of user arrival time at points of interest (PoIs) and then get the inter-user contact probability. Next, an efficient prediction-based user-recruitment strategy for mobile crowdsensing is proposed to minimize the data uploading cost. We then propose PURE-DF by extending PURE to a case in which we address the tradeoff between the delivery ratio of sensing data and the recruiter number according to Delegation Forwarding. We conduct extensive simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The results show that, compared with other recruitment strategies, PURE achieves a lower recruitment payment and PURE-DF achieves the highest delivery efficiency.

In this paper, we study how to address the sparsity, accuracy and serendipity issues of top-N recommendation with collaborative filtering (CF). Existing studies commonly use rated items (which form only a small section in a rating matrix) or import some additional information (e.g., details about the items and users) to improve the performance of CF. Unlike these methods, we propose a novel notion towards a huge amount of unrated items: serendipity item. By utilizing serendipity items, we propose concise satisfaction and interest injection (CSII), a method that can effectively find interesting, satisfying, and serendipitous items in unrated items. By preventing uninteresting and unsatisfying items to be recommended as top-N items, this concise-but-novel method improves accuracy and recommendation quality (especially serendipity) substantially. Meanwhile, it can address the sparsity and cold-start issues by enriching the rating matrix in CF without additional information. As our method tackles rating matrix before recommendation procedure, it can be applied to most existing CF methods, such as item-based CF, user-based CF and matrix factorization-based CF. Through comprehensive experiments using abundant real-world datasets with LensKit implementation, we successfully demonstrate that our solution improves the performance of existing CF methods consistently and universally. Moreover, comparing with baseline methods, CSII can extract uninteresting items more carefully and cautiously, avoiding potential items inferred by mistake.

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users, who provide the sensing services. The tasks, in practice, often have various spatiotemporal requirements, which make it hard to select suitable user set to perform the tasks. In this paper, we use the mobility prediction model to deal with this challenge and then propose the user selection algorithm to solve the user selection problem. From the perspective of mobility, it is probabilistic that the timesensitive tasks will be done on time and we use the time-related Markov model to achieve the probabilities. Furthermore, we consider the different uploading ways and obtain the corresponding probabilities, which could be used to propose a greedy user selection algorithm to select the suitable user set under the budget constraint. Extensive simulations have been conducted over two real-life mobile traces and the results prove the efficiency of our proposed algorithm.

Delay Tolerant Networks (DTNs) are featured by unpredictable mobility patterns and easily-interrupted connections. Forwarding strategy has always been the research focus, in order to improve the delivery ratio. An enormous amount of researches pay attention to solving the following two problems: whether to forward, and how to forward. Therefore, forwarding metrics and forwarding strategies both play important roles in DTNs. In this paper, we consider a generalized random-waypoint model with heterogeneous nodes, the node's speed is regarded as the forwarding metric, which includes the short-term and long-term speed. Subsequently, we propose a multi-copy Delegation Forwarding based on Short-term and Long-term speed in DTNs (DFSL), which first determines a comprehensive mapping from short-term speed and long-term speed to the actual forwarding metric. Then, according to the forwarding metric and delegation forwarding strategy, DFSL utilizes some efficient nodes with higher forwarding metrics to assist in delivering messages, in order to improve the delivery ratio, while reducing the forwarding cost. Finally, we conduct simulations based on the synthetic mobility pattern and real trace. The results show that, compared with other multi-copy forwarding strategies, DFSL achieves the highest forwarding efficiency, which is defined as the result of delivery ratio divided by forwarding cost.

Online Social Networks (OSNs) have attracted intensive attention for the reason that they provide users a convenient platform to share ideas, post events, and disseminate messages. Each OSN user commonly owns multiple social applications (Facebook, Google Plus and Twitter, etc.). They enjoy disseminating messages within one particular social application as well as forwarding interesting information to other social applications. In OSNs, some time-insensitive messages (disaster warnings, virus alerts, and search notices, etc.) are badly in need of being disseminated to specific users or applications as soon as possible. However, sudden message dissemination among users is bound to put a significant burden on network resources. Taking the dissemination cost into consideration, we propose a lightweight Message Dissemination strategy for Minimizing Delay in OSNs (MDMD), which first defines the user's activeness according to the switch habit, among different social applications. Furthermore, depending on the activeness, an optimal user within each social application is selected to assist in disseminating the message. Simulation results show that, compared with other dissemination strategies, MDMD achieves the lowest average delay, and lower average hopcounts.

Due to the uncertainty of network topology and intermittent connectivity among nodes in Delay Tolerant Networks (DTNs), beaconing is used to detect probabilistic contacts. However, it causes the following new problem: beaconing frequency not only influences the probability of message transmission, but also affects the consumption rate of energy. Thus, putting forward a beaconing control strategy in energy-constrained DTNs becomes the key point. In this paper, we propose an efficient and dynamic beaconing control method DBCEC in energy-constrained DTNs based on the time-continuous Markov Model. A linear decline strategy (i.e. DBCEC-L) and exponential decay strategy (i.e. DBCEC-E) are respectively applied to control the beaconing frequency. Simulations based on the synthetic mobility model and real mobility traces are conducted in ONE, and results show that DBCEC-E achieves a better delivery rate without influencing average delay and the overhead ratio, compared with other beaconing control strategies.

Pocket Switched Networks (PSN) utilize both human mobility and occasional connectivity to transfer messages between mobile humans' devices. Recently, a large number of mobile phones have come into our daily lives. Therefore, the PSN composed of human-carried mobile phones will be an ubiquitous network environment in the near future. In this paper, it is proposed that the WiFi hotspot mode of a mobile phone is applied in the PSN, in order to realize the phone-to-phone communications. However, due to the lack of energy supply, a phone in hotspot mode could rapidly consume energy and shorten its battery lifetime significantly. To maximize the message dissemination scope within the limited energy constraint of each phone in PSN, an Energy efficient Phone-to-phone Communication method based on WiFi Hotspot (EPCWH) is presented to schedule the phone's switch between hotspot mode and client mode. Simulations based on the synthetic random-waypoint mobility pattern are conducted in ONE; the results show that EPCWH achieves the best performance in terms of message dissemination and energy consumption among different switch strategies.

Due to unpredictable node mobility and the easily-interrupted connections, routing protocols in DTNs commonly utilize multiple message copies to improve the delivery ratio. A store-carry-and-forward paradigm is also designed to assist routing messages. However, excessive message copies lead to rapid consumption of the limited storage and bandwidth. The spray and Wait routing protocol has been proposed to reduce the network overload caused by the storage and transmission of unrestricted message copies. However, there still exist congestion problems when a node's buffer is quite constrained. In this paper, we propose a message Scheduling and Drop Strategy on spray and wait Routing Protocol (SDSRP). To improve the delivery ratio, first of all, SDSRP calculates the priority of each message by evaluating the impact of both replicating and dropping a message copy on delivery ratio. Subsequently, scheduling and drop decisions are made according to the priority. Finally, we conduct extensive simulations based on synthetic and real traces in ONE. The results show that, compared with other buffer management strategies, SDSRP achieves higher delivery ratio, similar average hop counts, and lower overhead ratio.