In the digital age, the security and privacy of col-laborative systems are increasingly critical. This paper examines the asynchronous interactive learning environment within Delay Tolerant Networks (DTN), which leverages satellite broadcasting in remote areas. However, this environment encounters challenges such as weak infrastructure, unstable connections, and security vulnerabilities associated with open networks. Additionally, traditional public key cryptosystems are susceptible to threats posed by quantum computing. To mitigate these issues, we propose an anonymous communication scheme based on lattice theory and pseudonyms, designed to enhance secure network communication for remote and impoverished areas through collaboration among communication entities in the DTN environment. Our security analysis and performance comparison demonstrate that the pro-posed scheme outperforms existing lattice-based solutions while maintaining essential security characteristics.

Abstract Remote sensing image target detection provides an effective and accurate data analysis tool for many application areas. Due to complex backgrounds, large differences in target scales, and missed detection of small targets, remote sensing image target detection is challenging. In order to enhance the model's understanding of the global information of remote sensing images, this paper proposes the GFA module. This module can establish the global contextual connection of remote sensing images to provide rich context to help understand the complex scene and background in which the target is located, without being limited to local information. Additionally, it focuses on channel information for enhanced target feature extraction. For the purpose of alleviating the serious imbalance in foreground–background samples that is present in single‐level target detection models. The loss function is reconstructed based on focal loss by redefining the balance factor α and focus factor γ , so that it can be dynamically adjusted during network training. Meanwhile, EIoU is used to further enhance the bounding box regression capability. Affine transformations were also used to augment the dataset in order to assist the model in adjusting to real‐world situations. The proposed method is experimentally validated on the publicly available HRRSD dataset. In comparison with YOLO v5, the mAP of the detection results improved by 2.7%. Compared with YOLO v8 and YOLO v10, the mAP improved by 3.2% and 3.3%. The model achieves an FPS of 40.1, an optimal balance between speed and accuracy. Further, experiments are conducted using the NWPU VHR‐10 dataset and the RSOD dataset, both of which demonstrated that the proposed method outperforms other target detection methods and improves remote sensing target detection performance.

Image editing approaches with diffusion models have been rapidly developed, yet their applicability are subject to requirements such as specific editing types (e.g., foreground or background object editing, style transfer), multiple conditions (e.g., mask, sketch, caption), and time consuming fine-tuning of diffusion models. For alleviating these limitations and realizing efficient real image editing, we propose a novel editing technique that only requires an input image and target text for various editing types including non-rigid edits without fine-tuning diffusion model. Our method contains three novelties: (I) Target-text Inversion Schedule (TTIS) is designed to fine-tune the input target text embedding to achieve fast image reconstruction without image caption and acceleration of convergence. (II) Progressive Transition Scheme applies progressive linear interpolation between target text embedding and its fine-tuned version to generate transition embedding for maintaining non-rigid editing capability. (III) Balanced Attention Module (BAM) balances the tradeoff between textual description and image semantics. By the means of combining self-attention map from reconstruction process and cross-attention map from transition process, the guidance of target text embeddings in diffusion process is optimized. In order to demonstrate editing capability, effectiveness and efficiency of the proposed BARET, we have conducted extensive qualitative and quantitative experiments. Moreover, results derived from user study and ablation study further prove the superiority over other methods.

Existing saliency object detection (SOD) methods struggle to satisfy fast inference and accurate results simultaneously in high resolution scenes. They are limited by the quality of public datasets and efficient network modules for high-resolution images. To alleviate these issues, we propose to construct a saliency object matting dataset HRSOM and a lightweight network PSUNet. Considering efficient inference of mobile depolyment framework, we design a symmetric pixel shuffle module and a lightweight module TRSU. Compared to 13 SOD methods, the proposed PSUNet has the best objective performance on the high-resolution benchmark dataset. Evaluation results of objective assessment are superior compared to U2Net that has 10 times of parameter amount of our network. On Snapdragon 8 Gen 2 Mobile Platform, inference a single 640 × 640 image only takes 113ms. And on the subjective assessment, evaluation results are better than the industry benchmark IOS16 (Lift subject from background).

With the advent of the 5G era and the rapid development of emerging industries such as cloud computing and big data, data centers, as resource management platforms and key transmission hubs, are experiencing a dramatic increase in business data volume. Therefore, efficient routing optimization algorithms are needed. Improvements in Software Defined Networks (SDN) and programmable network devices allow different data traffic to be transmitted through various network paths, enabling fine-grained network performance optimization. In this paper, we utilize the emerging Hybrid Knowledge-Defined Network (KDN) architecture to propose a Multi-Agent Reinforcement Learning (MARL)-based multipath routing algorithm, termed RL-MR. RL-MR organizes intelligent agents to generate routes in a hop-by-hop manner, offering excellent scalability. The algorithm comprehensively considers flow latency, flow transmission rate, and packet loss rate to formulate routing strategies. To ensure reliability and accelerate the learning process, we introduce an auxiliary learning mechanism into RL-MR. Experiments conducted using Ryu and Mininet demonstrate that the RL-MR algorithm significantly outperforms baseline methods in terms of network throughput, link bandwidth utilization, and latency. Additionally, it exhibits excellent adaptability and reliability in scenarios involving flow changes, unknown large topologies, and partial deployments.

Image matting is a fundamental technique in visual understanding and has become one of the most significant capabilities in mobile phones. Despite the development of mobile storage and computing power, achieving diverse mobile Artificial Intelligence Generated Content (AIGC) applications remains a great challenge. To address this issue, we present an innovative demonstration of an automatic system called "Matting Moments" that enables automatic image editing based on matting models in different scenarios. Coupled with accurate and refined matting subjects, our system provides visual element editing abilities and backend services for distribution and recommendation that respond to emotional expressions. Our system comprises three components: 1) photo content structuring, 2) data-driven matting engine, and 3) AIGC functions for generation, which automatically achieve diverse photo beautification in the gallery. This system offers a unified framework that guides consumers to obtain intelligent recommendations with beautifully generated contents, helping them enjoy the moments and memories of their present life.

Delay Tolerant Network (DTN) is a network model designed for special environments. It is designed to be used in challenging network environments with high latency levels, bandwidth constraints, and unstable data transmission. It plays an important role in extremely special environments such as disaster rescue, maritime communication, and remote areas. Currently, research on DTN mainly focuses on innovative routing protocols, with limited research of the security issues and solutions. In response to the above problems, this paper analyzes and compares the security problems faced by delay tolerance networks and their solutions and security schemes.

Generative adversarial networks (GANs) have been widely adopted in many image processing tasks including restoration. In order to further improve quality of generated images, the training objective function needs to incorporate more constraints in addition to the adversarial loss. It can be straightforward to combine various losses in a linear fashion. However, hyperparameter fine-tuning and non-convex loss optimization are challenging problems when combining cost functions in such a manner. Here, we propose an efficient formulation of multiple loss components for training GANs. The proposed method, termed HypervolGAN, not only provides an efficient alternative for simultaneous cost optimization, but also boosts model performance in terms of improving generated image quality without excess computation. We further introduce two image-quality-measure based loss components to the GANs specifically for image restoration. Extensive evaluations and results on various benchmark datasets validate the effectiveness of the proposed methods.