Cross-modal hashing has emerged as a pivotal solution for efficient retrieval across diverse modalities, such as images and texts, by mapping them into compact binary hash spaces. However, in real-world scenarios, the modalities data is often missing or misaligned. Existing methods are most rely on fully paired training data and ignore missing or misaligned modalities data, resulting in the semantic inconsistencies. To address these challenges, we propose an Adaptive Graph Attention-Based Discrete Hashing (AGADH) method, which consists of three parts. First, to solve the problem of missing modalities, AGADH employs a masked completion strategy to reconstruct missing modalities. Second, to mitigate semantic misalignment, AGADH leverages a Graph Attention Network (GAT) encoder-decoder architecture with alignment module to construct features from different modalities. Additionally, to enhance the fusion performance, an adaptive fusion module dynamically adjusting the contributions of image and text modalities with learnable weighting coefficients is proposed. Extensive experiments on three benchmark datasets, MS-COCO, NUS-WIDE, and MIRFlickr-25K, demonstrating that AGADH outperforms state-of-the-art methods in both fully paired and incompletely paired scenarios, showing its robustness and effectiveness in cross-modal retrieval tasks.

In the context of the Internet of Things (IoT), the large-scale generation and collection of data can greatly improve the quality of service provided, but they also raise significant concerns about privacy breaches. However, existing privacy-preserving data collection solutions based on local differential privacy (LDP) often struggle to balance security and accuracy when handling composite data types. To address this challenge, in this paper, we propose CSKV, a high-precision and privacy-preserving key-value data collection scheme. Specifically, we first design a padding and sampling protocol to improve data utility. Then, we propose two randomized response mechanisms to safely perturb keys and values in a cohesive and segmented manner. After that, by leveraging the sampling protocol and key-value correlation perturbation, we demonstrate that CSKV can provide secondary privacy amplification. Detailed theoretical analysis verifies the security and effectiveness of CSKV. In addition, extensive performance evaluations are conducted on synthetic and real-world datasets, and the results indicate that our proposed scheme outperforms existing schemes in terms of hit rate and estimation variance.

The demand for multimodal data processing drives the development of information technology. Cross-modal hash retrieval has attracted much attention because it can overcome modal differences and achieve efficient retrieval, and has shown great application potential in many practical scenarios. Existing cross-modal hashing methods have difficulties in fully capturing the semantic information of different modal data, which leads to a significant semantic gap between modalities. Moreover, these methods often ignore the importance differences of channels, and due to the limitation of a single goal, the matching effect between hash codes is also affected to a certain extent, thus facing many challenges. To address these issues, we propose a Dynamic Masking and Auxiliary Hash Learning (AHLR) method for enhanced cross-modal retrieval. By jointly leveraging the dynamic masking and auxiliary hash learning mechanisms, our approach effectively resolves the problems of channel information imbalance and insufficient key information capture, thereby significantly improving the retrieval accuracy. Specifically, we introduce a dynamic masking mechanism that automatically screens and weights the key information in images and texts during the training process, enhancing the accuracy of feature matching. We further construct an auxiliary hash layer to adaptively balance the weights of features across each channel, compensating for the deficiencies of traditional methods in key information capture and channel processing. In addition, we design a contrastive loss function to optimize the generation of hash codes and enhance their discriminative power, further improving the performance of cross-modal retrieval. Comprehensive experimental results on NUS-WIDE, MIRFlickr-25K and MS-COCO benchmark datasets show that the proposed AHLR algorithm outperforms several existing algorithms.

The rapid expansion of vehicular networks has intensified congestion and privacy risks. Although Federated Learning (FL) addresses both challenges through decentralized model training while preserving data locality, existing FL-based client selection strategies often fall short in highly heterogeneous vehicular environments. Specifically, the inherent heterogeneity in vehicular networks—characterized by diverse data distributions and system resources—complicates current methods. The imbalanced nature of local data further exacerbates model divergence, resulting in degraded overall system performance. To solve the above problems, in the process of vehicle selection, this paper comprehensively considers three kinds of heterogeneity and proposes an FL model with adaptive proximal term. The weight coefficient of this model is dynamically adjusted based on the difference between the local and global model parameters. Based on this, a contribution score-based vehicle selection strategy (CSVS), considering the dynamics of vehicles, is proposed to alleviate the problem of traditional model weight divergence and minimize system cost. Experimental results on two classic datasets demonstrate that the proposed strategy significantly outperforms baseline methods in reducing system cost and improving model training performance, particularly in highly heterogeneous vehicular environments.

Due to its low storage cost and fast search speed, cross-modal retrieval based on hashing has attracted widespread attention and is widely used in real-world applications of social media search. However, most existing hashing methods are often limited by uncomprehensive feature representations and semantic associations, which greatly restricts their performance and applicability in practical applications. To deal with this challenge, in this paper, we propose an end-to-end graph attention network hashing (EGATH) for cross-modal retrieval, which can not only capture direct semantic associations between images and texts but also match semantic content between different modalities. We adopt the contrastive language image pretraining (CLIP) combined with the Transformer to improve understanding and generalization ability in semantic consistency across different data modalities. The classifier based on graph attention network is applied to obtain predicted labels to enhance cross-modal feature representation. We construct hash codes using an optimization strategy and loss function to preserve the semantic information and compactness of the hash code. Comprehensive experiments on the NUS-WIDE, MIRFlickr25K, and MS-COCO benchmark datasets show that our EGATH significantly outperforms against several state-of-the-art methods.

Owing to the growing proliferation of machine type communication devices and other high‐end devices in conventional human‐to‐human (H2H) communication, it is inevitable that these types of communications will co‐exist with each other in the next generation of cellular communications. This study investigates an uplink cellular network with machine‐to‐machine (M2M) and H2H co‐existence, where a machine type communication gateway is deployed as a relay in the cellular network to forward the M2M messages to the base station (BS). Non‐orthogonal multiple access (NOMA) has been adopted to transmit the data of H2H and M2M communications to the BS simultaneously. Considering the different delay‐sensitive transmissions of M2M/H2H communications, the expressions for outage probability and effective capacity (EC) are theoretically derived with the constraints of quality‐of‐service requirements. Simulation results show that the NOMA scheme outperforms the orthogonal multiple access in terms of outage probability and EC.

This paper investigates a power allocation problem for maximizing energy efficiency (EE) in downlink Coordinated Multi-Point (CoMP) systems with non- orthogonal multiple access (NOMA). First, users' achievable data rate and network throughput are analysed under three transmission schemes: 1) all users' signals are jointly transmitted by coordinated base stations (BSs); 2) only cell-edge users' signals are jointly transmitted by coordinated BSs; 3) each user's signals are transmitted by only one BS. Next, we formulate EE maximization problems for the three schemes under the constraints of minimum users' data rate and maximum BS transmit power. The considered problem is non-convex and hard to tackle. To address it, an iterative sub-optimal algorithm is proposed by adopting fractional programming and difference of convex programming. Numerical results show that the near optimality performance of EE can be achieved by using the proposed algorithm with advantages of fast convergence and low complexity. Three transmission schemes of NOMA have superior EE performance compared with conventional orthogonal multiple access scheme in the same CoMP networks.