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.

Ride-pooling significantly reduces traffic congestion by enhancing fleet utilization through effective ride-matching. Real-world ride-pooling systems are dynamic, with fluctuations in driver availability and demand throughout the day. This necessitates adaptive ride-matching strategies that can quickly adjust to changing proximities and identify new carpooling opportunities by recalculating driver-rider correlations. However, most current methods primarily focus on static demand-supply scenarios and short-term accessibility, falling short in dynamic environment. In this study, we introduce a dynamic heterogeneous network model that captures the evolving nature of ride-pooling systems, where new requests and carpooling arrangements continuously emerge. We propose an embedding model-based matching decision process that operates online, adjusting to changes in the network’s structure. This process involves constructing a dynamic heterogeneous ride-pooling network that encompasses diverse node attributes and driver-rider connections, updating these representations to reflect the network’s evolution, and quickly identifying and ranking candidate riders for efficient online matching. Our approach demonstrates improved performance in offline evaluations using datasets from Austin, TX (RideAustin) and Chengdu, China (DiDi Chuxing). We observe a reduction in the necessary fleet size as new orders are placed, and an improvement in drivers’ matching probability compared to existing methods (e.g., an increase of 5.4–31.1% in the assignment rate on DiDi dataset), showcasing the advantage of employing dynamic network embedding to cut down on matching time (e.g., a decrease of 3.7–228.8 seconds in running time on DiDi dataset). Furthermore, we develop a simulated ride-pooling system (SRPool) that mimics dynamic demand-supply fluctuations and supports vehicle routing, providing a robust platform for evaluating ride-matching strategies. Our strategy not only excels in the SRPool environment but also effectively minimizes the total trip distance and rider waiting times.

As one of the fundamental tasks of autonomous driving, depth perception aims to perceive physical objects in three dimensions and to judge their distances away from the ego vehicle. Although great efforts have been made for depth perception, LiDAR-based and camera-based solutions have limitations with low accuracy and poor robustness for noise input. With the integration of monocular cameras and LiDAR sensors in autonomous vehicles, in this article, we introduce a two-stream architecture to learn the modality interaction representation under the guidance of an image reconstruction task to compensate for the deficiencies of each modality in a parallel manner. Specifically, in the two-stream architecture, the multi-scale cross-modality interactions are preserved via a cascading interaction network under the guidance of the reconstruction task. Next, the shared representation of modality interaction is integrated to infer the dense depth map due to the complementarity and heterogeneity of the two modalities. We evaluated the proposed solution on the KITTI dataset and CALAR synthetic dataset. Our experimental results show that learning the coupled interaction of modalities under the guidance of an auxiliary task can lead to significant performance improvements. Furthermore, our approach is competitive against the state-of-the-art models and robust against the noisy input. The source code is available at https://github.com/tonyFengye/Code/tree/master .

Learning causality from large-scale text corpora is an important task with numerous applications—for example, in finance, biology, medicine, and scientific discovery. Prior studies have focused mainly on simple causality, which only includes one cause-effect pair. However, causality is notoriously difficult to understand and analyze because of multiple cause spans and their entangled interactions. To detect complex causality, we propose a self-paced contrastive learning model, namely N2NCause, to learn entangled interactions between multiple spans. Specifically, N2NCause introduces data enhancement operations to convert implicit expressions into explicit expressions with the most rational causal connectives for the synthesis of positive samples and to invert the directed connection between a cause-effect pair for the synthesis of negative samples. To learn the semantic dependency and causal direction of positive and negative samples, self-paced contrastive learning is proposed to learn the entangled interactions among spans, including the interaction direction and interaction field. We evaluated the performance of N2NCause in three cause-effect detection tasks. The experimental results show that, with the least data annotation efforts, N2NCause demonstrates competitive performance in detecting simple cause-effect relations, and it is superior to existing solutions for the detection of complex causality.

Identifying the emotional causes of mental illnesses is key to effective intervention. Existing emotion-cause analysis approaches can effectively detect simple emotion-cause expressions where only one cause and one emotion exist. However, emotions may often result from multiple causes, implicitly or explicitly, with complex interactions among these causes. Moreover, the same causes may result in multiple emotions. How to model the complex interactions between multiple emotion spans and cause spans remains under-explored. To tackle this problem, a contrastive learning-based framework is presented to detect the complex emotion-cause pairs with the introduction of negative samples and positive samples. Additionally, we developed a large-scale emotion-cause dataset with complex emotion-cause instances based on subreddits associated with mental health. Our proposed approach was compared to prevailing CNN-based, LSTM-based, Transformer-based and GNN-based methods. Extensive experiments have been conducted and the quantifiable outcomes indicate that our proposed solution achieves competitive performance on simple emotion-cause pairs and significantly outperformed baseline methods in extracting complex emotion-cause pairs. Empirical studies further demonstrated that our proposed approach can be used to reveal the emotional causes of mental disorders for effective intervention.

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.

In e-commerce platforms, the online descriptive information of products shows significant impacts on the purchase behaviors. To attract potential buyers for product promotion, numerous workers are employed to write the impressive product descriptions. The hand-crafted product descriptions are less-efficient with great labor costs and huge time consumption. Meanwhile, the generated product descriptions do not take consideration into the customization and the diversity to meet users’ interests. To address these problems, we propose one generic framework, namely DeepDepict, to automatically generate the information-rich and personalized product descriptive information. Specifically, DeepDepict leverages the graph attention to retrieve the product-related knowledge from external knowledge base to enrich the diversity of products, constructs the personalized lexicon to capture the linguistic traits of individuals for the personalization of product descriptions, and utilizes multiple pointer-generator network to fuse heterogeneous data from multi-sources to generate informative and personalized product descriptions. We conduct intensive experiments on one public dataset. The experimental results show that DeepDepict outperforms existing solutions in terms of description diversity, BLEU, and personalized degree with significant margin gain, and is able to generate product descriptions with comprehensive knowledge and personalized linguistic traits.

Over the last few years, many efforts have been devoted to revealing human mobility patterns. However, the regularity and variability of human mobility from a microscopic view, i.e., what factors affect human mobility patterns, has yet not been investigated. In this paper, we aim to study the impact factors that may affect the regularity and variability of human mobility patterns using social network analysis. Specifically, we introduce the spatial interaction matrix to represent the interaction strength and interaction semantics among spatial regions. Based on the spatial interaction matrix, we investigate the factors that impact the mobility patterns, including temporal factors, occupational factors and age factors. Our experimental results demonstrate that lots of factors such as environmental, temporal and age factors contribute to the shape of human mobility patterns.