Mobile edge systems require efficient resource synchronization to support coordination in dynamic, infrastructure-less environments, where traditional cloud-based approaches incur high overhead and limited robustness. We present PrudentCaster, a tunable decentralized broadcast gossip framework that improves synchronization efficiency by minimizing redundant transmissions. The key idea is to guide forwarding using a local, MLST-inspired structure, enabling selective dissemination while preserving high coverage without global coordination. We further introduce a unified metric that captures trade-offs among coverage, redundancy, latency, and bandwidth. Evaluation in both simulation and a small-scale robotic testbed shows that PrudentCaster improves synchronization efficiency by up to 1.6 × in real deployments and up to 6 × in large-scale scenarios.

Drone swarms are increasingly important in IoT applications such as agriculture, disaster response, and industrial inspection. However, effective service discovery remains challenging due to drones’ limited resources and the swarm’s dynamic topology. Existing solutions often suffer from congestion, single points of failure, and poor adaptability. To overcome these limitations, we propose a decentralized and dynamic service discovery framework tailored for drone swarms. Our approach models services using fine-grained sensor-level decomposition and leverages spatiotemporal information from drones to enable timely coordination. The core of the framework is a two-phase affinity propagation mechanism: a fully distributed clustering phase based on spatiotemporal leadership to provide a decentralized service registry, followed by a local adaptation phase for dynamic registry updates. To enhance reliability, a spatiotemporal-driven priority chain is used for service replication and failover. Extensive simulations and a case study in a wildfire suppression scenario across various swarm sizes show that our framework significantly outperforms centralized and existing clustering-based methods in efficiency, robustness with limited resources. This makes it a promising solution for reliable service discovery and flexible, fine-grained collaboration in drone swarms.

Video Moment Retrieval (VMR) aims to identify a temporal segment in an untrimmed video that best matches a given textual query. Bias in VMR is a critical issue, where the model achieves favorable results even if disregarding the video input. Existing evaluation methods, such as Resplitting, have attempted to address bias by creating out-of-distribution (OOD) datasets. However, these methods provide an incomplete definition of bias and do not quantify bias. To this end, we provide a comprehensive definition of bias in VMR, encompassing both data bias and model bias. Besides, our evaluation metrics can analyze the magnitude of these biases better. To address both data and model biases comprehensively, we introduce Reverse Distribution based VMR (ReDis-VMR). This novel approach dynamically generates datasets with inverse distributions tailored to different models based on Gaussian kernel estimation. As a result, it enables a more accurate evaluation of model performance. Building on ReDis-VMR, we further propose the Dynamic Expandable Adjustment (DEA) pipeline. DEA incrementally expands the model structure to enhance its focus on video and text features, and it incorporates a fair loss to minimize the influence of concentrated data distributions. The experimental results on bias ratio demonstrate that our ReDis method achieves state-of-the-art performance in bias elimination, while the results on moment retrieval confirm the effectiveness of our DEA framework across three evaluation methods, two datasets, and three baselines.

To mitigate data sparsity in Sequential Recommendation, Cross-Domain Sequential Recommendation (CDSR) exploits dynamic knowledge transfer across domains. Traditional CDSR approaches merge specific-domain sequences into mixed-domain sequences to reconnect users' dispersed interests. However, most methods rely on unidirectional transfer between mixed and specific domains on each domain task, overlooking the complex interplay between mixed-domain and domain-specific dynamics. Moreover, token-level transfer between coinciding domain sequences fails to consider inherent sequential dynamics. To address these limitations, we propose Multi-Domain Enhancement via Residual Interwoven Transfer (MERIT). Specifically, MERIT enhances domain representations along multiple domain-to-domain paths, leveraging the proposed extended cross-attention fusion compatible with partially overlapping sequences. To facilitate such transfers, MERIT further employs MoE networks in encoders to generate both intra-domain and inter-domain representations. In addition, by integrating stopped-gradient mixed-domain representations into specific-domain representations, MERIT enables the model to learn the residual signal of the mixed-domain information, better aligning with downstream specific-domain tasks. Extensive experiments on three real-world datasets demonstrate that MERIT consistently outperforms state-of-the-art CDSR counterparts with statistical significance.

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains.A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains.One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it.Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings.Another challenge lies in aligning the domain-specific and cross-domain sequences.Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains.In such cases, the domain-specific knowledge carried by the current tokens may degrade performance.To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI).Specifically, leveraging LoRA's effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation.First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics.Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain.Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths.

Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging (fMRI) data. Despite the prevalence of Graph Neural Networks (GNNs) and Graph Transformers in various domains, applying them to brain networks faces challenges. Specifically, the datasets are severely impacted by the noises caused by distribution shifts across sub- populations and the neglect of node identities, both obstruct the identification of disease-specific patterns. To tackle these challenges, we propose Contrasformer, a novel contrastive brain network Transformer. It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations by a two-stream attention mechanism. A cross attention with identity embedding highlights the identity of nodes, and three auxiliary losses ensure group consistency. Evaluated on 4 functional brain network datasets over 4 different diseases, Contrasformer outperforms the state-of-the-art methods for brain networks by achieving up to 10.8% improvement in accuracy, which demonstrates its efficacy in neurological disorder identification. Case studies illustrate its interpretability, especially in the context of neuroscience. This paper provides a solution for analyzing brain networks, offering valuable insights into neurological disorders. Our code is available at https://github.com/AngusMonroe/Contrasformer.

With the explosive growth of multimodal Internet data, cross-modal hashing retrieval has become crucial for semantically searching instances across different modalities. However, existing cross-modal retrieval methods rely on assumptions of perfect consistency between modalities and between modalities and labels, which often do not hold in real-world data. We introduce two types of inconsistency: Modality-Modality (M-M) and Modality-Label (M-L) inconsistencies. We further validate the prevalent existence of inconsistent data in multimodal datasets and highlight it will reduce the accuracy of existing Cross-Modal retrieval methods. In this paper, we propose a novel framework called Inconsistency Alleviated Cross-Modal Retrieval (IA-CMR), addressing challenges posed by these inconsistencies. We first utilize two forms of contrastive learning loss and a mutual exclusion constraint to effectively disentangle modal information into modality-common hash codes and modality-unique hash codes. Our dedicated design in modality disentanglement is capable of alleviating the M-M inconsistency. Subsequently, we refine common labels through a label refinement loss and employ a Cross-modal Common Semantic Alignment module for effective alignment. The label refinement process and the CCSA module collectively handle the M-L inconsistency issue. IA-CMR outperforms 9 comparison baselines on two benchmark multimodal datasets, achieving an improvement in retrieval accuracy of up to 25.13%. The results confirm the effectiveness of IA-CMR in alleviating inconsistency and enhancing cross-modal retrieval performance.

Cross-modal hashing has attracted considerable attention for large-scale multimodal data. Recent supervised cross-modal hashing methods using multi-label networks utilize the semantics of multi-labels to enhance retrieval accuracy, where label hash codes are learned independently. However, all these methods assume that label annotations reliably reflect the relevance between their corresponding instances, which is not true in real applications. In this paper, we propose a novel framework called Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval (Bi-CMR), which exploits a bidirectional learning to relieve the negative impact of this assumption. Specifically, in the forward learning procedure, we highlight the representative labels and learn the reinforced multi-label hash codes by intra-modal semantic information, and further adjust similarity matrix. In the backward learning procedure, the reinforced multi-label hash codes and adjusted similarity matrix are used to guide the matching of instances. We construct two datasets with explicit relevance labels that reflect the semantic relevance of instance pairs based on two benchmark datasets. The Bi-CMR is evaluated by conducting extensive experiments over these two datasets. Experimental results prove the superiority of Bi-CMR over four state-of-the-art methods in terms of effectiveness.