The explosion of multimedia data in information-rich environments has intensified the challenges of personalized content discovery, positioning recommendation systems as an essential form of passive data management. Multimodal sequential recommendation, which leverages diverse item information such as text and images, has shown great promise in enriching item representations and deepening the understanding of user interests. However, most existing models rely on heuristic fusion strategies that fail to capture the dynamic and context-sensitive nature of user-modal interactions. In real-world scenarios, user preferences for modalities vary not only across individuals but also within the same user across different items or categories. Moreover, the synergistic effects between modalities-where combined signals trigger user interest in ways isolated modalities cannot-remain largely underexplored. To this end, we propose CAMMSR, a Category-guided Attentive Mixture of Experts model for Multimodal Sequential Recommendation. At its core, CAMMSR introduces a category-guided attentive mixture of experts (CAMoE) module, which learns specialized item representations from multiple perspectives and explicitly models inter-modal synergies. This component dynamically allocates modality weights guided by an auxiliary category prediction task, enabling adaptive fusion of multimodal signals. Additionally, we design a modality swap contrastive learning task to enhance cross-modal representation alignment through sequence-level augmentation. Extensive experiments on four public datasets demonstrate that CAMMSR consistently outperforms state-of-the-art baselines, validating its effectiveness in achieving adaptive, synergistic, and user-centric multimodal sequential recommendation.

The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising alternative. The effectiveness of current DL techniques typically depends on the availability of high-quantity and high-accuracy datasets, which are yet difficult to obtain in large deformation problems. During the dataset construction process, a dilemma stands between data quantity and data accuracy, leading to suboptimal performance in the DL models. To address this challenge, we focus on a representative application of large deformations, the stretch bending problem, and propose FilDeep, a Fidelity-based Deep Learning framework for large Deformation of elastic-plastic solids. Our FilDeep aims to resolve the quantity-accuracy dilemma by simultaneously training with both low-fidelity and high-fidelity data, where the former provides greater quantity but lower accuracy, while the latter offers higher accuracy but in less quantity. In FilDeep, we provide meticulous designs for the practical large deformation problem. Particularly, we propose attention-enabled cross-fidelity modules to effectively capture long-range physical interactions across MF data. To the best of our knowledge, our FilDeep presents the first DL framework for large deformation problems using MF data. Extensive experiments demonstrate that our FilDeep consistently achieves state-of-the-art performance and can be efficiently deployed in manufacturing.

Recent advancements in multimodal recommendations, which leverage diverse modality information to mitigate data sparsity and improve recommendation accuracy, have gained significant attention. However, existing multimodal recommendations overlook the critical role of user representation initialization. Unlike items, which are naturally associated with rich modality information, users lack such inherent information. Consequently, item representations initialized based on meaningful modality information and user representations initialized randomly exhibit a significant semantic gap.

Spatial Crowdsourcing (SC) has become a popular paradigm for executing spatiotemporal tasks, such as urban sensing, ride-hailing, and last-mile delivery. In many of these service applications, it is common for workers to undertake multiple tasks simultaneously, as this allows them to efficiently complete tasks with a convenient route. In multi-task SC, location privacy, movement cost, and service quality constitute the three most critical factors. However, the inherent contradictions among these factors pose significant challenges in simultaneously ensuring them all, and to date, no existing work has addressed all three factors concurrently. To fill the research gap, in this paper, we propose a Privacy-Preserving, Cost-Aware, and Quality-Enabled Service Planning (PCQ-SP) scheme for multi-task SC. First, we introduce a secure distance acquisition method with additive homomorphic encryption and garbled circuits, which enables the calculation of distances between workers and tasks while preserving both their location privacy. Second, we design a Thompson Sampling-based quality evaluation method to assess each worker's service quality by balancing exploration and exploitation. Third, we propose a minimum-cost flow-driven service planning method to derive the optimized service plans, which aims for high quality and low cost while preserving location privacy. To our knowledge, our PCQ-SP scheme is the first to simultaneously consider location privacy, movement cost, and service quality in SC systems. Extensive evaluations demonstrate the effectiveness of our PCQ-SP scheme in enhancing service quality, reducing movement costs, and ensuring location privacy.

Cloud-fog Federated Learning (FL) is promising for collaborative model training in large-scale edge networks. In cloud-fog FL with constrained communication resources, selecting high-quality client models for aggregation is critical to boost the global model. However, the clients in real world hold multimodal and heterogeneous data, while existing selection strategies rarely consider the imbalance of communication cost and model convergence rates across modalities, thus seriously degrading the efficiency of model aggregation. Moreover, most previous multimodal fusion methods require aligned multimodal samples. However, the data of modality-heterogeneous clients may be isolated and unaligned in FL, so these previous methods cannot be applied to such scenarios, thereby hindering the knowledge fusion across various modalities. To address the above issues, we propose a cloud-fog FL system named COOL, which achievesunimodal aggregationat the fog layer andmultimodal fusionat the cloud layer. First, we propose a Modality-aware Online Client Selection (MOCS) strategy to assist unimodal aggregation. Unlike previous selection strategies, MOCS realizes the dynamic selection budget allocation for various modalities by monitoring the convergence gap of modalities, thus striking the performance balance among modality-heterogeneous clients. Second, to overcome the limitation of previous methods that require aligned multimodal data, we propose a Multimodal Fusion strategy with Feature Synthesis (MFFS). MFFS realizes multimodal fusion with isolated samples via adaptive feature synthesis and cross-modal attention training, thus building a more powerful multimodal predictor while preserving the client data privacy. Finally, experimental results demonstrate that COOL has superior performance compared to the existing algorithms.

In Mobile Crowd Sensing (MCS), privacy and quality are two pivotal issues. Specifically, Bilateral Location Privacy Preservation (BLPP) and Bilateral Data Privacy Preservation (BDPP) represent the most critical dual-bilateral privacy in MCS. However, recruiting high-quality workers typically necessitates calculating the spatial proximity of each worker-task pair and the trustworthiness of the reported data, which tends to introduce significant dual-bilateral privacy risks. Additionally, due to the lack of prior knowledge about the workers' trustworthiness at the initial stage, the platform also faces the exploration-exploitation dilemma in worker recruitment. Existing work either overlooks the service quality of worker recruitment or fails to preserve the dual-bilateral privacy, making these two issues have not yet been adequately addressed. To bridge the gaps by addressing the associated challenges, this paper proposes aQuality-awareDual-BilateralPrivacyPreserving (Q-DBPP) scheme, for upholding service quality under both BLPP and BDPP. Specifically, we present two perturbation-based privacy preservation stages to calculate Degree of Proximity (DoP) and Degree of Trust (DoT) while safeguarding BLPP and BDPP, respectively. Meanwhile, to address the exploration-exploitation dilemma, we employ an upper confidence bound-based high-quality worker recruitment stage, which integrates the estimated DoT and DoP into the reverse auction to comprehensively optimize service quality. To the best of our knowledge, our Q-DBPP scheme is the first to simultaneously achieve high service quality and dual-bilateral privacy in MCS. Theoretical analyses and extensive experiments validate the superior performance of our Q-DBPP scheme in terms of privacy, incentive, efficiency, and quality.

Concept Bottleneck Models (CBMs) enhance the interpretability of AI systems, particularly by bridging visual input with human-understandable concepts, effectively acting as a form of multimodal interpretability model. However, existing CBMs typically assume static datasets, which fundamentally limits their adaptability to real-world, continuously evolving multimodal data streams. To address this, we define a novel continual learning task for CBMs: simultaneously handling concept-incremental and class-incremental learning. This task requires models to continuously acquire new concepts (often representing cross-modal attributes) and classes while robustly preserving previously learned knowledge. To tackle this challenging problem, we propose CONceptual Continual Incremental Learning (CONCIL), a novel framework that fundamentally re-imagines concept and decision layer updates as linear regression problems. This reformulation eliminates the need for gradient-based optimization, thereby effectively preventing catastrophic forgetting. Crucially, CONCIL relies solely on recursive matrix operations, rendering it highly computationally efficient and well-suited for real-time and large-scale multimodal data applications. Experimental results compellingly demonstrate that CONCIL achieves ''absolute knowledge memory'' and significantly surpasses the performance of traditional CBM methods in both concept- and class-incremental settings, thus establishing a new paradigm for continual learning in CBMs, particularly valuable for dynamic multimodal understanding.

2D images and 3D point clouds are foundational data types for multimedia applications, including real-time video analysis, augmented reality (AR), and 3D scene understanding. Class-incremental semantic segmentation (CSS) requires incrementally learning new semantic categories while retaining prior knowledge. Existing methods typically rely on computationally expensive training based on stochastic gradient descent, employing complex regularization or exemplar replay. However, stochastic gradient descent-based approaches inevitably update the model's weights for past knowledge, leading to catastrophic forgetting, a problem exacerbated by pixel/point-level granularity. To address these challenges, we propose CFSSeg, a novel exemplar-free approach that leverages a closed-form solution, offering a practical and theoretically grounded solution for continual semantic segmentation tasks. This eliminates the need for iterative gradient-based optimization and storage of past data, requiring only a single pass through new samples per step. It not only enhances computational efficiency but also provides a practical solution for dynamic, privacy-sensitive multimedia environments. Extensive experiments on 2D and 3D benchmark datasets such as Pascal VOC2012, S3DIS, and ScanNet demonstrate CFSSeg's superior performance.

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches.

In pursuit of an immersive virtual experience within the Cyber-Physical Metaverse Systems (CPMS), the construction of Avatars often requires a significant amount of real-world data. Mobile Crowd Sensing (MCS) has emerged as an efficient method for collecting data for CPMS. While progress has been made in protecting the privacy of workers, little attention has been given to safeguarding task privacy, potentially exposing the intentions of applications and posing risks to the development of the Metaverse. Additionally, existing privacy protection schemes hinder the exchange of information among entities, inadvertently compromising the quality of the collected data. To this end, we propose a Quality-aware and Obfuscation-based Task Privacy-Preserving (QOTPP) scheme, which protects task privacy and enhances data quality without third-party involvement. The QOTPP scheme initially employs the insight of “showing the fake, and hiding the real” by employing differential privacy techniques to create fake tasks and conceal genuine ones. Additionally, we introduce a two-tier truth discovery mechanism using Deep Matrix Factorization (DMF) to efficiently identify high-quality workers. Furthermore, we propose a Combinatorial Multi-Armed Bandit (CMAB)-based worker incentive and selection mechanism to improve the quality of data collection. Theoretical analysis confirms that our QOTPP scheme satisfies essential properties such as truthfulness, individual rationality, and ε-differential privacy. Extensive simulation experiments validate the state-of-the-art performance achieved by QOTPP.

Mobile Crowd Sensing is a prevalent and efficient paradigm for multi-source data collection, where Multi-source Data Fusion (MDF) plays a crucial role in constructing quality data collection services. Current MDF methods often require the majority of participating sensing sources to be credible, or assume that the workers’ credibility is either prior known or easily calculable. However, due to the presence of uncredible environments and the problem of Information Elicitation Without Verification (IEWV), these methods are impractical. It may lead to a vicious cycle where the recruitment of uncredible workers affects the quality of the estimated truth, which can further lead to misjudgments of worker credibility, thereby exacerbating the quality of subsequent recruitment. In this article, a Reliable Multi-source Data Fusion scheme with Cross Validation (RMDF-CV) is proposed to obtain reliable truth for service construction. Specifically, we first introduce the Combinatorial Multi-Armed Bandit (CMAB) model to recruit high-credibility workers by balancing exploration and exploitation. Then, we establish three-stage truth data through three different data sources: Unmanned Aerial Vehicles, credible workers, and Deep Matrix Factorization. Theoretical analyses and extensive simulations confirm the excellent performance of our RMDF-CV scheme.

In the service of Mobile Crowd Sensing (MCS), High-quality Data Collection (HDC), Bilateral Location Privacy Preservation (BLPP), and sensing cost are three pivotal issues. It is widely believed that HDC necessitates the recruitment of workers with high Quality of Service (QoS), which is related to the sensing data capabilities of the recruited workers and the worker-task distances. However, submitting high-quality data demands more resources from the workers, incurring higher costs. Meanwhile, BLPP techniques, aiming to conceal the locations of the workers and tasks, may impede the evaluation of the workers' QoS. Therefore, there is still a lack of a low-cost and BLPP high QoS data collection research. Motivated by this, we propose a Quality-Aware, Low-Cost, and Privacy-Preserving Data Collection Service (QLP-DCS) for MCS. First, we propose a matrix perturbation-based approach to achieve BLPP while preserving the partial order relationship of distances. Subsequently, we employ the Upper Confidence Bound indexes-based reverse auction recruiting workers to balance exploration and exploitation with the low sensing cost. Then, we propose a multi-level truth discovery approach and establish an effective trust verification mechanism. Theoretical analysis and extensive experiments validate the superior performance of our QLP-DCS.

The Energy Internet (EI) aims to build a sustainable energy ecosystem by connecting diverse energy sources and prosumers. Mobile Crowd Sensing (MCS) enables efficient data collection for monitoring and aggregation from distributed devices. Given the complex behavior of workers driven by self-interest, recruiting trustworthy, high-quality, and inexpensive workers remains a significant challenge in research and practice. Previous studies often assume that worker characteristics are known or can be obtained after data collection. However, evaluating worker qualities is quite challenging in the face of multi-source data and complex workers. To address this, we propose a Completion Rate Learning based Multi-Armed Bandit reverse Auction (CRL-MABA) scheme for identifying and selecting high-quality workers in MCS. Our CRL-MABA scheme first proposes a Spatial-Temporal Upper Confidence Bound (STUCB) method to recruit workers, considering both the quality of workers for exploitation and the spatiotemporal features for exploration. In addition, the Dual-Stage Data Estimation Mechanism (DSDEM) and Long-Term and Short-Term Memory Learning (LTSTML) are designed to identify workers accurately and efficiently. Importantly, our proposed scheme avoids the impractical assumptions in previous works while satisfying important criteria such as truthfulness, individual rationality, and computational efficiency. The effectiveness of our scheme is demonstrated through extensive experimental results, which show its superiority over existing strategies.

Mobile crowdsourcing (MCS) is an emerging paradigm that harnesses the collective power of the crowd to tackle large-scale tasks. To ensure the high-quality worker selection, various combinatorial multiarmed bandit (CMAB)-based schemes have been proposed. However, previous schemes often overlook critical issues. First, the post-unknown worker recruitment (PUWR) problem emerges when the quality of a worker remains unknown despite reported worker data. Second, the presence of Sybil Requesters is often neglected, who manipulate ratings to deceive workers for malicious purposes. To tackle these challenges, we present an innovative scheme called bi-directional trust verification-based CMAB (BTV-CMAB). First, we propose a truth quality discovery approach that effectively addresses the PUWR problem by estimating worker quality. Additionally, we employ a BTV mechanism to assess the Degree of Trust (DoT) of requesters and the reputation of workers. To select top-notch workers for MCS, we combine the worker quality and reputation into an upper confidence bound (UCB) index. The effectiveness of the BTV-CMAB scheme is supported by theoretical proof, which demonstrates its ability to ensure truthfulness and individual rationality. Furthermore, experimental results reveal promising improvements achieved by our scheme, including a 17.44%, increase in the platform’s revenue and a significant decrease in regret of up to 88.26%. To the best of our knowledge, this study is the first to propose utilizing a BTV mechanism to effectively address the PUWR problem and counter the threat of Sybil attacks in the CMAB-based worker recruitment process.

The widespread adoption of mobile smart devices has ushered in the era of Mobile Crowd Sensing (MCS), serving as an efficient method for large-scale data collection. Inherently location-sensitive, the service construction of MCS faces a crucial challenge of Location Privacy Preservation (LPP). Prior studies for LPP often necessitate a Trusted Third Party (TTP), which is not always feasible. Moreover, these privacy-preserving techniques may inadvertently obscure dishonest or malicious behaviors, leading to compromised Quality of Service (QoS). Motivated by this, we propose a Quality-enabled Bilateral Location Privacy-Preserving (Q-BLPP) service construction scheme, ensuring Bilateral LPP without TTP, while maintaining QoS. To achieve bilateral LPP, we introduce a novel BI-LBE algorithm using Bloom Indexing (BI) and Location-Based Encryption (LBE). Additionally, for high-quality recruitment, we present a Combinatorial Multi-Armed Bandit (CMAB) approach to balance exploration and exploitation. Furthermore, to ensure privacy during recruitment, worker profiles are anonymized using differential privacy. To our knowledge, our approach is the first to integrate QoS and LPP in MCS, with theoretical proofs of truthfulness and individual rationality. Simulations demonstrate that our Q-BLPP scheme strikes a favorable balance between computational efficiency, privacy security, and service quality, outperforming existing schemes.