LLM-based empathetic dialogue systems enhance agents' emotional support capabilities. Previous approaches primarily relied on Chain-of-Thought (CoT) prompting to extract key dialogue cues and further strengthened the agent's sensitivity to these signals through supervised fine-tuning. However, such methods overly depend on the information extraction capability of LLMs, leading to unstable reasoning and limited interpretability. To simultaneously improve an agent's ability to proactively explore solutions through rational reasoning while attending to users' sensitive emotions via empathetic understanding, we propose Neuro-Sym Supporter, a hybrid decision-making emotional support agent that integrates symbolic reasoning with deep learning. This model combines rational inference with emotional empathy, enabling the agent to generate supportive responses that balance logic and emotion. Specifically, we introduce Sym-Mind, a differentiable logic-based reasoning framework for emotional support strategy selection, which unifies interpretability with stable performance. Experimental results on public datasets demonstrate that our approach consistently outperforms multiple competitive baselines in both automatic and human evaluations, validating its effectiveness.

Persuasion, a vital social skill, influences beliefs, attitudes, and behaviors through conversation. Yet, current dialogue agents either rely on scenario-specific strategies, restricting their cross-context adaptability, or neglect persuasion’s logical structure. They focus on isolated strategy classification, overlooking the significance of fine-grained sequential planning for real-world scenarios. To address these limitations, inspired by basic human mental activities, we present PersuHSG, an adaptive persuasion strategy planning framework. The core idea is to conceptualize persuasion as a tripartite framework comprising cognition, affection, and volition, with each stage represented as a graph layer and principle-based strategies for efficient multi-stage persuasion. Specifically, we first develop PersuInstruct, a fine-tuning dataset to improve dialogue agents’ strategic planning and response generation. Then, we propose a graph-aware planning algorithm for stage-strategy-response reasoning to generate persuasive responses for diverse scenarios. Extensive experiments confirm that PersuHSG significantly enhances the persuasiveness of Large Language Models (LLMs), allows smaller models (e.g., 9B, 13B) to achieve competitive performance, and demonstrates the efficacy of structured strategy planning in improving model efficiency and adaptability.

Efficient order dispatching is crucial for online ride-hailing systems, directly influencing user experience and platform revenue. Traditional methods often focus on maximizing immediate revenue through local observations of individual vehicles, ignoring the long-term potential benefits , the dynamic nature of dispatching systems, and the importance of collaboration among distributed vehicles. This typically results in suboptimal performance. To address these issues, we propose FedMARL4OD, a novel Federated Multi-Agent Deep Reinforcement Learning framework designed to optimize order dispatching. This framework integrates local learning via Multi-Agent Reinforcement Learning (MARL) for individual vehicles and global learning via Federated Multi-Agent Reinforcement Learning (FedMARL) across all vehicles. Specifically, we introduce an innovative reward mechanism in local learning that considers both the current revenue of each order and the supply–demand dynamics of the system related to potential future revenue, thereby improving dispatching performance. Moreover, we introduce a scalable model aggregation method in global learning that explicitly models interactions among distributed vehicles to facilitate collaborative learning . By progressively integrating local and global insights through average parameter aggregation, this method not only reduces communication overhead and enhances the learning efficiency of agents, but also ensures system scalability and maintains data privacy. Extensive real-world simulations demonstrate that FedMARL4OD outperforms baseline methods , achieving a 9.17% increase in Accumulated Driver Income (ADI) and a 7.75% improvement in Order Response Rate (ORR). The ADI improvements demonstrate the framework’s effectiveness in boosting revenue, while the enhanced ORR indicates a quicker fulfillment of users’ requests, improving user experience.

Edge Federated Learning (EFL) has demonstrated significant potential in the field of Artificial Intelligence of Things (AIoT) by protecting data privacy and reducing communication costs. However, in real-world scenarios, multiple independent edge networks seldom collaborate due to factors such as data heterogeneity and the absence of a central server. Mobile devices, acting as bridges across different environments, offer an opportunity to enable dynamic collaboration among multiple edge networks. Nevertheless, as mobile devices transition between edge networks, they may encounter new classes with only a few samples, leading to catastrophic forgetting of previous knowledge and overfitting in new environments. To address this challenge, we propose Cross-F$^{2}$SCIL, a Federated Few-Shot Class Incremental Learning method that enables on-demand dynamic collaboration in mobile edge network environments. Cross-F$^{2}$SCIL allows mobile devices to efficiently learn new class knowledge from few-shot samples upon entering new edge networks while consolidating prior knowledge to prevent forgetting. Specifically, to mitigate the forgetting caused by new class overwriting on devices and parameter dilution at the server, we design a two-phase training framework. In the first phase, we learn a local model using Prototype Augmentation to enhance the retention of prior knowledge. In the second phase, we obtain the global model via Hierarchical Personalized Parameter Aggregation to effectively integrate learned knowledge across devices. To effectively learn new class information while reducing overfitting, we incorporate Self-Supervised Knowledge Aggregation and Prototype Knowledge Fusion to enhance model generalization and seamlessly integrate new classes into the existing model. Compared to the best-performing baseline on each dataset, Cross-F$^{2}$SCIL achieves an average improvement of 5.52% in Average Accuracy across five datasets, with the maximum improvement reaching 7.97%.

Mobile Edge Computing (MEC) leverages embedded devices to deliver low-latency and cost-effective intelligent services, such as autonomous driving. Federated learning allows MEC devices to collaboratively learn a global model without exposing private data. However, the dynamic real-world environments that MEC devices operate in can lead to catastrophic forgetting of previously learned knowledge. While continual learning has been applied in federated learning to retain knowledge about past data, there are significant barriers when extending it for evolving MEC environments due to unbearable communication and computation costs for resource-constrained devices. We address this by proposing Sparse-FCL, a federated continual learning framework that uses sparse training to reduce training overhead and improve model performance. Specifically, to retain the generalization knowledge of specific tasks, we introduce a progressive neuron selection via multi-device collaboration module, which gradually selects the key neurons that show importance on multiple devices in each continual learning task. In addition, we propose a task-adaptive topology exploration module, aiming to provide more pliable dynamic sparse training configurations for the scenarios of federated continual learning. Experiments on FCL benchmarks demonstrate Sparse-FCL's superior accuracy under high sparsity levels, and it also achieves reductions of 80.4%, 89.5%, and 19.7% in communication, computation, and storage overhead, respectively, compared to existing federated continual learning methods.

Traffic prediction is one of the fundamental spatio-temporal prediction tasks in urban computing, which is of great significance to a wide range of applications, e.g., traffic controlling, vehicle scheduling, etc. Recently, with the expansion of the city and the development of public transportation, long-range and long-term spatio-temporal correlations play a more important role in traffic prediction. However, it is challenging to model long-range spatial dependencies and long-term temporal dependencies simultaneously in two aspects: 1) complex influential factors, including spatial, temporal and external factors. 2) multiple spatio-temporal correlations, including long-range and short-range spatial correlations, as well as long-term and short-term temporal correlations. To solve these issues, we propose a spatio-temporal memory augmented multi-level attention network for fine-grained traffic prediction, entitled ST-MAN. Specifically, we design a spatio-temporal memory network to encode and memorize fine-grained spatial information and representative temporal patterns. Then, we propose a multi-level attention network to explicitly model both short-term local spatio-temporal dependencies and long-term global spatio-temporal dependencies at different spatial scales (i.e., grid and region levels) and temporal scales (i.e., daily and weekly levels). In addition, we design an external component that takes external factors and spatial embeddings as inputs to generate location-aware influence of the external factors much more efficiently. Finally, we design an end-to-end framework optimized with the contrastive objective and supervised objective to boost model performance. Empirical experiments over coarse-grained and fine-grained real-world datasets demonstrate the superiority of the ST-MAN model compared to several state-of-the-art baselines.

Federated Learning (FL) in mobile edge computing (MEC) systems has recently been studied extensively. In ubiquitous environments, there are usually cross-edge devices that learn a series of tasks across multipleindependentedge FL systems. Due to the differences in the scenarios and tasks of different FL systems, cross-edge devices will forget past tasks after learning new tasks, which is unacceptable for devices that pay system costs to participate in FL. Continual learning (CL) is a viable solution to this problem, which aims to train a model to learn a series of tasks without forgetting old knowledge. Currently, there is no work to investigate the problem of CL in a cross-edge FL scenario. In this paper, we proposeCross-FCL, aCross-edgeFederatedContinualLearning framework. Specifically, it enables devices to retain the knowledge learned in the past when participating in new task training through a parameter decomposition based FCL model. Then various cross-edge strategies are introduced, including biased global aggregation and local optimization, to trade off memory and adaptation. We conducted experiments on a real-world dataset and other public datasets. Extensive experiments demonstrate that Cross-FCL achieves best accuracy on IID and highly non-IID tasks with a low storage cost compared to other baselines.

Explainability is crucial for the successful use of AI for fake news detection (FND). Researchers aim to improve the explainability of FND by highlighting important descriptions in crowd-contributed comments as clues. From the perspective of law and sociology, there are distinct clues that are easy to discover and understand, and faint clues that require careful observation and analysis. For example, in fake news related to COVID-Omicron showing increased pathogenicity and transmissibility, distinct clues might involve virologists’ opinions regarding the inverse correlation between pathogenicity and transmissibility. Meanwhile, faint clues might be reflected in an infected person’s claim that the symptoms are milder than a cold (indirectly indicating reduced pathogenicity). Occasionally, the statements of some ordinary eyewitnesses can decisively reveal the truth of the news, leading to the judgment of fake news. Existing methods generally use static networks to model the entire news life-cycle, which makes it fail to capture the subtle dynamic interactions between individual clues and news. Thereby faint clues, whose relations to the truth of news are challenging to be characterized and extracted directly, are more likely to be overshadowed by distinct clues. To address this issue, we propose an explainable FND method, dubbed as PiercingEye, which leverages dynamic interaction information to progressively mine valuable clues. PiercingEye models the news propagation topology as a dynamic graph, with interactive comments serving as nodes, and employs the time-semantic encoding mechanism to refine the modeling of temporal interaction information between comments and news to preserve faint clues. Subsequently, it utilizes the self-attention mechanism to aggregate distinct and faint clues for FND. Experimental results demonstrate that PiercingEye outperforms state-of-the-art methods and is capable of identifying both faint and distinct clues for humans to debunk fake news.

Mobile crowd sensing and computing (MCSC) has become a hot research area in recent years. This article presents our vision of the next generation of MCSC, crowd intelligence with the deep fusion of human, machine, and Internet of Things (IoT), namely, CrowdHMT. It aims to build a self-organizing, self-learning, self-adaptive, and continuous-evolving smart space with the deep fusion of Crowdsourced human, machine, and IoT intelligence. This article first characterizes the concept of CrowdHMT. We further investigate its challenges and techniques, and present its main application areas. Finally, we make discussions about the open issues and future research directions of CrowdHMT.

The blooming of fake news on social networks has devastating impacts on society, the economy, and public security. Although numerous studies are conducted for the automatic detection of fake news, the majority tend to utilize deep neural networks to learn event-specific features for superior detection performance on specific datasets. However, the trained models heavily rely on the training datasets and are infeasible to apply to upcoming events due to the discrepancy between event distributions. Inspired by domain adaptation theories, we propose an end-to-end adversarial adaptation network, dubbed as MetaDetector , to transfer meta knowledge (event-shared features) between different events. Specifically, MetaDetector pushes the feature extractor and event discriminator to eliminate event-specific features and preserve required meta knowledge by adversarial training. Furthermore, the pseudo-event discriminator is utilized to evaluate the importance of news records in historical events to obtain partial knowledge that are discriminative for detecting fake news. Under the coordinated optimization among all the submodules, MetaDetector accurately transfers the meta knowledge of historical events to the upcoming event for fact checking. We conduct extensive experiments on two real-world datasets collected from Sina Weibo and Twitter. The experimental results demonstrate that MetaDetector outperforms the state-of-the-art methods, especially when the distribution discrepancy between events is significant.

Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users’ preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines.

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

In recent years, takeout ordering and delivery (TOD) has become an emerging service due to its convenience and efficiency. However, current online ordering platforms still suffer from some issues, such as limited delivery coverage and delayed delivery. To address these issues, we propose a spatial crowdsourcing (SC)-based system called crowd delivery network (CrowDNet) to have packages take hitchhiking rides with existing taxis. We first tackle passenger riding queries based on an evolutionary algorithm and then insert appropriate food delivery requests into a partial schedule with an improved insertion approach. Finally, we propose a ranking module based on the modern portfolio theory to recommend the delivery path, which can achieve a balance between the delivery cost and timely services. Evaluations based on three real-world datasets demonstrate that our proposed algorithms outperform baseline methods.

Store site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users’ preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low- and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models.

This paper builds a Food Delivery Network (FooDNet in short) using spatial crowdsourcing (SC). It investigates the participation of urban taxis to support on demand take-out food delivery. Unlike existing SC-enabled service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, two on demand food delivery problems under different situations are studied in our work: (1) for O-OTOD, the food is opportunistically delivered by taxis when carrying passengers, and the optimization goal is to minimize the number of selected taxis to maintain a relatively high incentive to the participated drivers; (2) for D-OTOD, taxis dedicatedly deliver food without taking passengers, and the aim is to minimize the number of selected taxis (i.e., to raise the reward for each participant) and the total traveling distance to reduce the cost. A two-stage approach, including the construction algorithm and the Adaptive Large Neighborhood Search (ALNS) algorithm based on simulated annealing, is proposed to solve the problem. We have conducted extensive experiments based on the real-world datasets, including city-wide restaurant data, cell tower data, and the large-scale taxi trajectory data with 10,000 taxis in the city of Chengdu, China. Experimental results demonstrate that our proposed algorithms are more effective and efficient than baselines, fulfilling the food delivery service using a smaller number of taxis within the given time.

Spatial crowdsourcing (SC) is an emerging paradigm of crowdsourcing, which commits workers to move to some particular locations to perform spatio-temporal-relevant tasks (e.g., sensing and activity organization). Task allocation or worker selection is a significant problem that may impact the quality of completion of SC tasks. Based on a conceptual model and generic framework of SC task allocation, this paper first gives a review of the current state of research in this field, including single task allocation, multiple task allocation, low-cost task allocation, and quality-enhanced task allocation. We further investigate the future trends and open issues of SC task allocation, including skill-based task allocation, group recommendation and collaboration, task composition and decomposition, and privacypreserving task allocation. Finally, we discuss the practical issues on real-world deployment as well as the challenges for large-scale user study in SC task allocation.

Navigation services using digital maps make people's travel much easier. However, these services often fail to provide specific routes to those destinations that lack micro data in digital maps, such as a small laundry store in a shopping area. In this paper, we propose CrowdNavi, a last mile navigation service in outdoor environments using crowdsourcing based on the guider-follower model. First, we collect trajectories of guiders and images of reference objects along trajectories. To guide followers by reference objects along the route, we design a Semantic Crowd Navigation model to generate fine-grained maps by integrating guiders' data. Second, we design two score functions to fulfill two main requirements and plan hints. Last, we provide context-aware navigation for followers based on the fine-grained map and detect deviation in real-time. Real world experiments conducted in three different areas show that our proposed system in combination with images of reference objects is efficient.

This paper builds a Food Delivery Network (FooDNet) that investigates the usage of urban taxis to support on demand take-out food delivery by leveraging spatial crowdsourcing. Unlike existing service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, we study the food delivery problem in association with the Opportunistic Online Takeout Ordering & Delivery service (O-OTOD). Specifically, the food is delivered incidentally by taxis when carrying passengers in the O-OTOD problem, and the optimization goal is to minimize the number of selected taxis to maintain a relative high incentive to the participated drivers. The two-stage method is proposed to solve the problem, consisting of the construction algorithm and the Large Neighborhood Search (LNS) algorithm. Preliminary experiments based on real-world taxi trajectory datasets verify that our proposed algorithms are effective and efficient.

Worker selection is a key issue in mobile crowd sensing (MCS). While the previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, a multitask-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multitask MCS environments. We study the problem of multitask worker selection under two situations: worker selection based on workers' intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two greedy-enhanced genetic algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).

Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance. (2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).