Distributed randomness beacons are essential for distributed systems (e.g., blockchain and MPC), providing un biased and unpredictable shared randomness. However, implementations in asynchronous networks often suffer from poor scal ability, low throughput, and high resource consumption when deployed as independent protocols. The state-of-the-art HashRand (CCS'24) achieves high throughput and low computational in tensity using only lightweight post-quantum cryptographic primitives for an independent asynchronous beacon. Building further on this, we propose Rubato, a low-overhead, high-throughput beacon protocol that leverages lightweight batched Asynchronous Complete Secret Sharing with Byzantine Atomic Broadcast-based state machine replication (via our tailored RubatoSMR). Rubato reduces communication complexity by an O(clogn) factor compared to HashRand and resolves the circular dependency between beacon and BAB-SMR in asynchronous settings. Experiments on AWSdemonstrate that Rubato achieves ideal overall performance in scalability, resource usage, and throughput; for instance, at n = 121nodes, it produces an average of 174 beacons per minute, with RubatoSMR further optimizing memory and bandwidth consumption.

With the rise of AI-Generated Content (AIGC) services, deployments within edge-cloud environments are becoming increasingly prevalent. Containerization offers resource isolation, lightweight deployment, and portability, making it a suitable technology for AIGC services. However, deploying AIGC services often requires large container images, leading to high deployment latency and bandwidth consumption. Based on real-world trace analysis showing the long-tail effect, where a few popular images account for the majority of requests, there is strong potential for optimizing caching mechanisms. This pattern can result in frequent cache misses and increased bandwidth consumption, especially under heavy load. In this paper, we propose a ValueOptimized Distributed Caching Mechanism (VODCM), which dynamically optimizes caching policies through a value-driven framework combined with deep reinforcement learning (DRL). VODCM prioritizes high-value images based on access frequency, layer size, and network latency, significantly improving cache hit rates and reducing network overhead. Preliminary evaluations show that VODCM enhances cache efficiency and reduces network and resource demands, offering an effective solution for AIGC image management in edge-cloud environments.

The development of the mobile Metaverse has garnered increasing attention in the next-generation Internet, fueled by the rapid advancements of mobile Internet, communication, and computing technologies. With the resource limitations faced by mobile Metaverse users (MUs), the mobile Metaverse market is flourishing. This market enables MUs to access high-quality immersive experiences by trading resources with Metaverse service providers (MSPs) across geographically distributed resource pools. However, the mobile Metaverse market still faces several challenges, includingthe hierarchical mobile Metaverse service structure, temporal dependencies, and heterogeneous incentive mechanisms. To address these problems, this paper proposes a dual-level resources trading approach for mobile Metaverse based on blockchain. This approach employs a dual-level structure consisting of resource provisioning and heterogeneous auction mechanisms. Specifically, we formulate the resource provisioning as a temporal-dependent average delay minimization problem at the low level. To solve this low-level problem, we introduce a novel algorithm calledLyDif, which leverages Lyapunov optimization techniques and diffusion models. At the high level, we propose a price-guided double dutch auction (PG-DDA) mechanism to match heterogeneous resources and determine pricing strategies. The PG-DDA smart contract is deployed on a consortium blockchain platform, facilitating resource trading management and transaction monitoring. Based on a real trace of edge-cloud service requests, our experimental results demonstrate the effectiveness of our proposed scheme in achieving optimal latency and social welfare.

The presence of Metaverse has elicited escalating attention in the next-generation Internet, followed by a large number of computationally intensive tasks, such as augmented reality, virtual reality, artificial intelligence-generated content (AIGC) applications, etc. With the popularity of mobile communication technology, mobile metaverse is becoming increasingly widespread. The resources required for these applications are rapidly growing in parallel with increasing demands from mobile Metaverse users (MUs), putting pressure on Metaverse service providers (MSPs) with limited resources, especially in mobile computing scenarios. Inspired by the burgeoning communication and computing technologies, the mobile Metaverse market between mobile MUs and MSPs is developing vigorously. However, there still remain numerous challenges in this market, including hierarchical mobile Metaverse structure, temporal dependencies, as well as heterogeneous incentive. In this paper, we propose a bi-alternating resource provisioning and heterogeneous auction approach for mobile Metaverse, named Bi-Meta. At the high level, resource provisioning is formulated as a Lyapunov problem minimizing average delay, solved by a novel bi-level based generative adversarial network, i.e., BiGAN. At the low level, a price- guided double dutch auction (PG-DDA) mechanism is presented for heterogeneous resource matching, with the designed PG- DDA smart contract. Based on the realistic edge-cloud company's traces, the experimental results verify that our proposed scheme achieves optimal latency and social welfare.

With rapid worldwide spread of Coronavirus Disease 2019 (COVID-19), jointly identifying severe COVID-19 cases from mild ones and predicting the conversion time (from mild to severe) is essential to optimize the workflow and reduce the clinician's workload. In this study, we propose a novel framework for COVID-19 diagnosis, termed as Structural Attention Graph Neural Network (SAGNN), which can combine the multi-source information including features extracted from chest CT, latent lung structural distribution, and non-imaging patient information to conduct diagnosis of COVID-19 severity and predict the conversion time from mild to severe. Specifically, we first construct a graph to incorporate structural information of the lung and adopt graph attention network to iteratively update representations of lung segments. To distinguish different infection degrees of left and right lungs, we further introduce a structural attention mechanism. Finally, we introduce demographic information and develop a multi-task learning framework to jointly perform both tasks of classification and regression. Experiments are conducted on a real dataset with 1687 chest CT scans, which includes 1328 mild cases and 359 severe cases. Experimental results show that our method achieves the best classification (e.g., 86.86% in terms of Area Under Curve) and regression (e.g., 0.58 in terms of Correlation Coefficient) performance, compared with other comparison methods.

In most word-level lipreading architectures of recent years, temporal feature extraction module tend to employ Multi-scale Temporal Convolution Network (MS-TCN). In our experiments, we have noticed it is hard for MS-TCN to deal with noise information that may contain in image sequences. In order to solve the problems, we propose a lipreading architecture based on temporal shrinkage network and NetVLAD. We first propose Temporal Shrinkage Unit according to Residual Shrinkage Network and then replace temporal convolution unit with it. The improved network which named Multi-scale Temporal Shrinkage Network (MS-TSN) could focus more on relevant information. Following with MS-TSN that deals with noise frames, NetVLAD is proposed to integrate local information into global feature. Compared with Global Average Pooling, NetVLAD could extract key features by clustering. Our experiments on Lipreading in the Wild (LRW) show that the architecture we propose achieves an accuracy of 89.41%, attaining new state-of-the-art in word-level lipreading. In addition, we build a new Mandarin Chinese lipreading dataset named MCLR-100 and verify our proposed architecture on it.

Lesion detection is one of the most important issues in the field of ultrasound image analysis. Recent studies exploit convolutional neural networks to either classify the nodules or locate them with bounding boxes (detection-based algorithms). However, the training of these methods demand considerable and laborious object-level annotations labeled by ultrasound experts and may lack the explicit border information for accurate localization. To reduce the annotation cost and improve the borders feature of proposal box, we propose a novel Border Sensitive Network (BSNet) that extract border features from the extreme point of the borders in proposal box to enhance the single point feature and use a vector to represent arbitrary distribution of border locations. Experimental results demonstrate that BSNet achieves state-of-the-art performance compared with other weakly supervised methods in Thyroid Ultrasound Images dataset, and further analysis reflects their value in clinical practice.

Link prediction is the main task of knowledge graph completion, predicting missing relations between entities based the existing links among the entities. The problem of knowledge graph completion can be framed as a third-order binary tensor completion problem. In this case, tensor decomposition seems like a natural solution. And many previous studies have shown that tensor decomposition methods are superior to Trans-based methods in link prediction experiments. Typical tensor decomposition methods are Canonical Polyadic (CP) decomposition and Tucker decomposition. In this paper, we propose Block term decomposition Embedding model (BTDE) for link prediction based on Block term decomposition (which can be seen as a combination of CP decomposition and Tucker decomposition) of the binary tensor representation of knowledge graph triples. The embeddings learned through BTDE is interpretable. In addition, we prove BTDE is fully expressive and derive the bound on its entity and relation embedding dimensionality for full expressivity which is the same as TuckER and smaller than the bound of previous start-of-the-art models ComplEx and SimplE. We show empirically that BTDE outperforms most previous state-of-the-art models across five standard link prediction datasets.