To ensure the security and reliability ofInternet of Things(IoT) systems, blockchain technology has been widely introduced. TheByzantine Fault Tolerant(BFT) consensus protocol, as the core of blockchain, has thus attracted significant research interest. To enable parallel data processing, a recent line of BFT consensus protocols has adoptedDirected Acyclic Graph(DAG) structures, giving rise to DAG-BFT protocols such as BullShark and GradedDAG. These protocols all depend on randomly selected leaders to drive consensus. Yet, in the presence of malicious or slow leaders, mainstream DAG-BFT protocols remain susceptible to significant latency spikes. To mitigate this, recent studies have incorporated reputation-based mechanisms to enhance leader selection in DAG-BFT protocols. However, existing reputation-based protocols still face two fundamental liveness issues, arising from network latency and assumptions of a partially synchronous network. To address these issues, we propose Mako, an asynchronous reputation-based DAG consensus protocol with a dual-path design. Mako employs an optimistic path in which replicas accumulate scores based on their contributions; faster replicas are then preferentially selected as leaders to achieve low latency. Under adverse conditions, Mako transitions to a pessimistic path to guarantee liveness. However, the dual-path execution introduces an additional challenge: ensuring the consistency of blocks committed along the optimistic path and the pessimistic path. To resolve this challenge, we introduce a temporary leader and a rollback mechanism in the pessimistic path.We implement Mako and conduct an extensive experimental evaluation. The results show that Mako significantly outperforms state-of-the-art protocols. In particular, at the 28-node scale, Mako reduces latency by up to 51.8% and improves throughput by up to 26.7% compared to BullShark.

Sea state estimation (SSE) is critical to the safety of maritime transport and the reliability of autonomous ships. The frequency of different sea states varies significantly, leading to uneven data distribution. Existing deep learning methods for SSE typically focus on feature extraction, often using simple splicing and fusion, which can result in cross-domain incoherence and degrade model performance. Addressing sea state classification imbalance is often done through distance-based classifiers (e.g., prototype classifiers), but these can be less sensitive to minority classes, and using few prototypes for a class limits the expression of intra-class variations. To overcome these challenges, we propose the Temporal Spectral Cross Fusion Network (TSCFNet), which extracts temporal and spectral features. These are integrated via an innovative temporal spectral cross fusion module to maximize their complementary advantages. Additionally, we introduce a multi-fusion loss function, including temporal, spectral, and fusion losses, to optimize features across different dimensions. This approach improves the performance for minority classes and captures intra-class differences more effectively, solving the problem of category imbalance. Experimental results show that TSCFNet significantly outperforms baseline methods on two imbalanced sea state datasets and multiple multivariate spatio-temporal datasets.

As a promising distributed computing paradigm, edge computing (EC) enhances service quality and reduces service latency by deploying computational and storage resources at the network edge. However, due to edge servers' geographic distribution and resource constraints, EC is challenged by edge denial-of-service (EDDoS) attacks. Although various approaches have been proposed to mitigate EDDoS attacks, the impact of capacity uncertainties in edge server processing capacity and transmission latency has been largely overlooked. This limits the adaptability and robustness of mitigation strategies in edge computing environments, which could result in overload and paralysis of edge servers. To address this issue, this paper introduces uEDDoS-D, an uncertainty-aware EDDoS mitigation approach based on an improved deep deterministic policy gradient algorithm. uEDDoS-D models the uncertainties of computing capacities and transmission latency of edge servers in EC environments and leverages the collective computational resources of edge servers to mitigate EDDoS attacks without relying on traditional attack detection mechanisms. uEDDoS-D aims to minimize the impact of uncertainties in edge server processing capacity and transmission latency on mitigation decisions while effectively reducing service latency. Experimental results demonstrate that uEDDoS-D significantly reduces service latency, outperforming the state-of-the-art approaches by an average of 16.75%, and enhances system robustness in EDDoS attack scenarios.

Achieving pixel-level segmentation with low computational cost using multimodal data remains a key challenge in crack segmentation tasks. Existing methods lack the capability for adaptive perception and efficient interactive fusion of cross-modal features. To address these challenges, we propose a Lightweight Adaptive Cue-Aware Vision Mamba network (LIDAR), which efficiently perceives and integrates morphological and textural cues from different modalities under multimodal crack scenarios, generating clear pixel-level crack segmentation maps. Specifically, LIDAR is composed of a Lightweight Adaptive Cue-Aware Visual State Space module (LacaVSS) and a Lightweight Dual Domain Dynamic Collaborative Fusion module (LD3CF). LacaVSS adaptively models crack cues through the proposed mask-guided Efficient Dynamic Guided Scanning Strategy (EDG-SS), while LD3CF leverages an Adaptive Frequency Domain Perceptron (AFDP) and a dual-pooling fusion strategy to effectively capture spatial and frequency-domain cues across modalities. Moreover, we design a Lightweight Dynamically Modulated Multi-Kernel convolution (LDMK) to perceive complex morphological structures with minimal computational overhead, replacing most convolutional operations in LIDAR. Experiments on three datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods. On the light-field depth dataset, our method achieves 0.8204 in F1 and 0.8465 in mIoU with only 5.35M parameters. Code and datasets are available at https://github.com/Karl1109/LIDAR-Mamba.

To improve the throughput of Byzantine Fault Tolerance (BFT) consensus protocols, the Directed Acyclic Graph (DAG) topology has been introduced to parallel data processing, leading to the development of DAG-based BFT consensus. However, existing DAG-based works heavily rely on Reliable Broadcast (RBC) protocols for block broadcasting, which introduces significant latency due to the three communication steps involved in each RBC. For instance, DAGRider, a representative DAG-based protocol, exhibits the best latency of 12 steps, considerably higher than non-DAG protocols like PBFT, which only requires 3 steps. To tackle this issue, we propose LightDAG, which replaces RBC with lightweight broadcasting protocols such as Consistent Broadcast (CBC) and Plain Broadcast (PBC). Since CBC and PBC can be implemented in two and one communication steps, respectively, LightDAG achieves low latency.In our proposal, we present two variants of LightDAG, namely LightDAG1 and LightDAG2, each providing a trade-off between the best latency and the expected worst latency. In LightDAG1, every block is broadcast using CBC, which exhibits a best latency of 5 steps and an expected worst latency of 14 steps. Since CBC cannot guarantee the totality property, we design a block retrieval mechanism in LightDAG1 to assist replicas in retrieving missing blocks. LightDAG2 utilizes a combination of PBC and CBC for block broadcasting, resulting in the best latency of 4 steps and an expected worst latency of 12(t+1) steps, where t represents the number of actual Byzantine replicas. Since a Byzantine replica may equivocate through PBC, LightDAG2 prohibits blocks from directly referencing contradictory blocks. To ensure liveness, we propose a mechanism to identify and exclude Byzantine replicas if they engage in equivocation attacks. Extensive experiments have been conducted to evaluate LightDAG, and the results demonstrate its feasibility and efficiency.

To parallelize data processing within BFT consensus protocols,Directed Acyclic Graph(DAG) structures have been integrated into consensus design, shaping the realm of DAG-based BFT protocols. Existing DAG-based protocols rely on theReliable Broadcast(RBC) protocol or its variants for block dissemination, which ensures consistency and totality properties of the data delivery. However, the inherent communication overhead ofO(n2) in RBC (wherenis the total replica count) results in an unwieldyO(n3) overhead in current DAG-based solutions, as each replica disseminates blocks through RBC in parallel. In response to this issue, we propose two new broadcast protocols:Provable Broadcast(PBC) andEnhanced ProvableBroadcast (EPBC). Both PBC and EPBC maintain the consistency property of data delivery, similar to RBC, while offering linear communication overhead without totality. Leveraging these broadcast protocols, we devise Wahoo, a novel DAG-based BFT protocol that significantly reduces communication overhead toO(n2). To address the absence of the totality property, we introduce a block retrieval mechanism to assist replicas in acquiring missing blocks. Additionally, under favorable conditions, Wahoo achieves a low latency of 4δ (where δ symbolizes the actual network delay), rivaling the best performance of existing DAG-based protocols. Various experiments showcase Wahoo’s high performance, owing to its substantially reduced communication overhead.

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

To enable parallel processing, the Directed Acyclic Graph (DAG) structure is introduced to the design of asyn-chronous Byzantine Fault Tolerant (BFT) consensus protocols, known as DAG-based BFT. Existing DAG-based BFT protocols operate in successive waves, with each wave containing three or four Reliable Broadcast (RBC) rounds to broadcast data, resulting in high latency due to the three communication steps required in each RBC. For instance, Tusk, a state-of-the-art DAG-based BFT protocol, has a good-case latency of 7 communication steps and an expected worst latency of 21 communication steps. To reduce latency, we propose GradedDAG, a new DAG-based BFT consensus protocol based on our adapted RBC called Graded RBC (GRBC) and the Consistent Broadcast (CBC), with each wave consisting of only one GRBC round and one CBC round. Through GRBC, a replica can deliver data with a grade of 1 or 2, and a non-faulty replica delivering the data with grade 2 can ensure that more than 2/3 of replicas have delivered the same data. Meanwhile, through CBC, data delivered by different non-faulty replicas must be identical. In each wave, a block in the GRBC round will be elected as the leader. If a leader block has been delivered with grade 2, it and all its ancestor blocks can be committed. GradedDAG offers a good-case latency of 4 communication steps and an expected worst latency of 7.5 communication steps, significantly lower than the state-of-the-art. Experimental results demonstrate GradedDAG's feasibility and efficiency.

The popularity of blockchain technology has revived interest in Byzantine Fault Tolerance (BFT) consensus protocols. However, existing protocols suffer from high latency, especially when the system is deployed in a worldwide manner. Taking Practical Byzantine Fault Tolerance (PBFT), the best-known and de-facto standard of BFT consensus protocol, as an example, it requires at least three phases to commit a request. Although some works attempt to shorten the number of phases by proposing a fast-path commitment rule, they either sacrifice resilience or subvert security.

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multitask multiview learning in this article. We first explain the feasibility and advantages of multitask multiview learning for these two tasks. Then we propose a novel model named MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multiview graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view the attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network data sets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks.

Homomorphic Encryption (HE) allows processing cipher-text data, but it is a challenge to enable complex methods such as multimedia decompression in the HE domain. In this paper, we propose a novel scheme to enable FLAC (Free Lossless Audio Codec) decompression in the HE domain. FLAC applies linear prediction to predict the current sample and Golomb coding to encode residuals. FLAC decoding relies heavily on dynamic controls that HE does not support due to unknown values of control variables after encryption. Our scheme regularizes dynamic controls in FLAC decoding with static controls by calculating an encrypted matching bit for each possible value of a control variable and producing candidate results as if it were a match. The summation of each possible value’s candidate results multiplied by its matching bit is equivalent to selecting the results of the matched control value. Our FLAC decoding scheme enables Single-Instruction Multiple-Data (SIMD): multiple (e.g., 256) plaintexts are packed and encrypted into a single ciphertext, and decoding one encrypted frame corresponds to decoding multiple plaintext frames. Our scheme is applicable to other audio compression standards based on similar technologies. Experimental results are also reported.