The most efficient asynchronous Byzantine fault tolerant (BFT) framework innagreement settings is due to Ben-Or, Kemler, and Rabin (BKR), wherenis the total number of replicas. Despite recent efforts to bring BKR closer to practical deployment, state-of-the-art designs are still hampered by the inherent mutual waiting between the broadcast and agreement phases. In response, we propose ByzTopia, a new asynchronous BFT protocol that removes this performance bottleneck. Its technical core is to decouple these two phases without introducing additional cryptographic primitives. To enable a more efficient decoupling of the broadcast and agreement phases, we introduce multi-shot reliable broadcast (MRBC), which ensures that replicas deliver messages in a well-ordered sequence across consecutive slots. We implement ByzTopia and evaluate it in various settings. Experimental results show that ByzTopia achieves up to 6.54× the throughput of PACE (for n == 31), the state-of-the-art asynchronous BFT of the same type, and 2.65× that of FIN (for n = 16), the state-of-the-art signature-free asynchronous BFT.

Anonymous credential systems (ACs) enable users to selectively disclose attributes in an anonymous and unlinkable manner, but their misuse is hard to address without traceability. Existing traceable ACs introduce regulators to handle this issue, but they impose substantial communication and computational overhead on regulators, especially in complex scenarios involving large numbers of authentication records and multiple independent regulators. In this work, we propose Public-Key Encryption with Equality Test and Variable Public Generator (PKEET-VPG) and its verifiable version (VPV) for adapting to traceable ACs to support secure outsourceable record retrieval from regulators to service providers. The core technical idea is a session-user-level tracing key whose validity is bound to a single authentication session, thereby preventing the abuse of tracing keys.We formally prove that both schemes achieve OW-CCA2 security, and show that, when integrated into ACs, they preserve user anonymity, support non-frameability, and retain cross-session unlinkability. Theoretical analysis and comparative experiments demonstrate that our schemes can reduce the overhead of regulators at a moderate cost in the authentication phase. Furthermore, our schemes support session-level ciphertext deduplication, which may be of independent interest in some scenarios, such as anonymous voting or one-action-per-session authentication.

The widespread deployment of Internet of Things (IoT) devices has driven their segmentation into distinct trust domains for the purpose of governance, creating a critical need for secure cross-domain authentication (CDA). CDA must preserve both anonymity and traceability of device identities to enable trustworthy data exchange. However, existing approaches, while exploring this trade-off, remain vulnerable to single points of failure and Sybil attacks—threats that are especially severe for unattended and resource-constrained devices. In this paper, we propose a Self-Sovereign and Supervised Cross-domain authentication scheme (SCross) to tackle these issues. The main building block we designed is a pseudonym management scheme (PMS) that allows devices to generate and use pseudonyms without relying on a trusted party. Although devices has full control of their identities, PMS still ensures traceability, Sybil resistance, and revocability. We define the formal security models of PMS, instantiate it under two different approaches, namely group signature (SCross-GS) and zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs, SCross-ZK), and present security proofs for our proposal. We implemented and evaluated SCross. The result shows that our scheme achieves an effective trade-off between security and efficiency.

Recent real-time semantic segmentation models, whether single-branch or multi-branch, achieve good performance and speed. However, their speed is limited by multi-path blocks, and some depend on high-performance teacher models for training. To overcome these issues, we propose Golden Cudgel Network (GCNet). Specifically, GC-Net uses vertical multi-convolutions and horizontal multi-paths for training, which are reparameterized into a single convolution for inference, optimizing both performance and speed. This design allows GCNet to self-enlarge during training and self-contract during inference, effectively becoming a "teacher model" without needing external ones. Experimental results show that GCNet outperforms existing state-of-the-art models in terms of performance and speed on the Cityscapes, CamVid, and Pascal VOC 2012 datasets. The code is available at https://github.com/gyyang23/GCNet.

The task of referring video object segmentation (RVOS) involves segmenting objects in video frames based on a given text description. However, most existing approaches treat the text directly as a query, neglecting the valuable visual and temporal information from the video. This limitation may cause the query unable to accurately perceive the target object. To address this issue, we introduce a visual-guided query with temporal interaction for referring video object segmentation (VQTI) approach. Our method capitalizes on frame-level features and video-level features to guide the query generation process, resulting in an enhanced perception of the target object. In addition, we introduce a spectral-guided segmentation optimizer module to enhance the fine-grained information, leading to more precise segmentation masks. Extensive experiments shows competitive performance against state-of-the-art approaches.

Multi-scale features are crucial in encoding objects with varying scales in vision tasks. The classic top-down and bottom-up feature pyramid networks are a common strategy for multi-scale feature extraction. However, these approaches suffer from the loss or degradation of feature information, which impairs the fusion effect of non-adjacent levels. In this paper, we propose an Asymptotic Feature Pyramid Network (AFPN) that supports direct interaction between non-adjacent levels. AFPN starts by fusing two adjacent low-level features and asymptotic incorporates higher-level features into the fusion process. This fusion way avoids the significant semantic gap between non-adjacent levels. Adaptive spatial fusion operation is further used to mitigate potential multi-object information conflicts during feature fusion at each spatial location. To reduce parameters, computational requirements, and inference speed, we propose a Lightweight Asymptotic Feature Pyramid Network (LightAFPN) that uses the concept of reparametrization. We evaluate the proposed method on the MS-COCO 2017, PASCAL VOC and Cityscapes datasets in both object detection and semantic segmentation frameworks. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at https://github.com/gyyang23/AFPN.

Asynchronous common subset (ACS) is an essential building block for Byzantine fault-tolerance and multi-party computation. The classic ACS framework is due to Ben-Or, Kemler, and Rabin (BKR), consisting of${n}$reliable broadcast (RBC) instances and${n}$asynchronous binary agreement (ABA) instances (where${n}$is the total number of replicas). Despite recent progresses of practical BKR-ACS, the state-of-the-art designs are still trapped by a validity-latency tradeoff. In this paper, we propose Sweeper, a new ACS protocol that breaks the tradeoff, achieving optimal validity and latency. Moreover, Sweeper maintains other benefits including optimal resilience, signature-free, and information-theoretic settings. Sweeper is built on RBC and composable biased reproposable ABA (CBiased RABA). Different from the conventional RABA, CBiased RABA allows replicas to be more biased towards specific RABA instances. We provide generic strategies to transform existing ABA/RABA protocols and a new RABA protocol that we introduce, to CBiased RABA. Furthermore, Sweeper can achieve up to$2 \times $the throughput of PACE-ACS (CCS 2022), the state-of-the-art ACS protocol that follows the BKR-ACS framework.

Lane detection is a special task in autonomous driving. Its most prominent inherent feature is to learn the imagination of severely occluded objects. Traditional CNN-based networks learning the imagination tend to perform poorly. In this work, we propose a novel architecture, called Cycle_accumulation-Transformer (CaT), which is the first structure to handle the lane detection by fusing CNN and Transformer. In particular, Cycle_accumulation structure and Transformer structure complement each other, and they adopt the four-direction cyclic accumulation process of “up to down”, “down to up”, “left to right” and “right to left” in the convolutional mode and the self-attention mechanism of “QKV” to fuse global information respectively. Our method is based on pixel-level semantic segmentation with high detection accuracy while meeting real-time requirements. Moreover, our proposed method achieves state-of-the-art results on the Tusimple and also achieves competitive results on the CULane.

Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks. A common strategy for multi-scale feature extraction is adopting the classic top-down and bottom-up feature pyramid networks. However, these approaches suffer from the loss or degradation of feature information, impairing the fusion effect of non-adjacent levels. This paper proposes an asymptotic feature pyramid network (AFPN) to support direct interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent low-level features and asymptotically incorporates higher-level features into the fusion process. In this way, the larger semantic gap between non-adjacent levels can be avoided. Given the potential for multi-object information conflicts to arise during feature fusion at each spatial location, adaptive spatial fusion operation is further utilized to mitigate these inconsistencies. We incorporate the proposed AFPN into both two-stage and one-stage object detection frameworks and evaluate with the MS-COCO 2017 validation and test datasets. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at https://github.com/gyyang23/AFPN.

Summary Blockchain has started to appear as a potentially reliable and underlying technology for various fields. There have been lots of surveys focusing on blockchain with respect to specific topics, such as security, architecture, applications, and so on. However, a systematic mapping study, including all related fields about blockchain, has been largely ignored. In this article, we revisit the problem of complex networks in the form of scientific collaboration networks. More specifically, we utilize the method of systematic mapping and implement them into blockchain technology. We collect 233 articles by searching Baidu scholar with the keyword “blockchain,” then construct two complex networks according to the relationship of keywords and authors, respectively. The keywords' complex network is a small‐world network while the authors' complex network is not. Furthermore, the tool of Netdraw provides a visualized graph for the complex network. Meanwhile, we find some subgroups in the network, which may highlight the future direction of blockchain.

At present, clients can outsource lots of complex and abundant computation, e.g., Internet of things (IoT), tasks to clouds by the “pay as you go” model. Outsourcing computation can save costs for clients and fully utilize the existing cloud infrastructures. However, it is hard for clients to trust the clouds even if blockchain is used as the trusted platform. In this article, we utilize the verification method as SETI@home by only two rational clouds, who hope to maximize their utilities. Utilities are defined as the incomes of clouds when they provide computation results to clients. More specifically, one client outsources two jobs to two clouds and each job contains n tasks, which include k identical sentinels. Two clouds can either honestly compute each task or collude on the identical sentinel tasks by agreeing on random values. If the results of identical sentinels are identical, then client regards the jobs as correctly computed without verification. Obviously, rational clouds have incentives to deviate by collusion and provide identical random results for a higher income. We discuss how to prevent collusion by using deposits, e.g., bit-coins. Furthermore, utilities for each cloud can be automatically assigned by a smart contract. We prove that, given proper parameters, two rational clouds will honestly send correct results to the client without collusion.

Partially observable Markov decision process (POMDP) is an extension to MDP. It handles the state uncertainty by specifying the probability of getting a particular observation given the current state. DESPOT is one of the most popular scalable online planning algorithms for POMDPs, which manages to significantly reduce the size of the decision tree while deriving a near-optimal policy by considering only $K$ scenarios. Nevertheless, there is a gap in action selection criteria between planning and execution in DESPOT. During the planning stage, it keeps choosing the action with the highest upper bound, whereas when the planning ends, the action with the highest lower bound is chosen for execution. Here, we propose LB-DESPOT to alleviate this issue, which utilizes the lower bound in selecting an action branch to expand. Empirically, our method has attained better performance than DESPOT and POMCP, which is another state-of-the-art, on several challenging POMDP benchmark tasks.

The partially observable Markov decision process (POMDP) provides a general framework for modeling an agent's decision process with state uncertainty, and online planning plays a pivotal role in solving it. A belief is a distribution of states representing state uncertainty. Methods for large-scale POMDP problems rely on the same idea of sampling both states and observations. That is, instead of exact belief updating, a collection of sampled states is used to approximate the belief; instead of considering all possible observations, only a set of sampled observations are considered. Inspired by this, we take one step further and propose an online planning algorithm, Adaptive Online Packing-guided Search (AdaOPS), to better approximate beliefs with adaptive particle filter technique and balance estimation bias and variance by fusing similar observation branches. Theoretically, our algorithm is guaranteed to find an $\epsilon$-optimal policy with a high probability given enough planning time under some mild assumptions. We evaluate our algorithm on several tricky POMDP domains, and it outperforms the state-of-the-art in all of them.

Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi-selfish miners), who mine on the private chain, to mine on public chain with a small probability ρ. Simulation results show that SMHMDP can trade off between revenues and forking rate. Put differently, selfish miners benefit from attacking within an acceptable forking rate toward the view of honest parties, without leading selfish mining attacks to be an armchair strategist.

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. As an important part of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in military, transportation, medical, and household fields. However, in the applications of wireless sensor networks, the adversary can infer the location of a source node and an event by backtracking attacks and traffic analysis. The location privacy leakage of a source node has become one of the most urgent problems to be solved in wireless sensor networks. To solve the problem of source location privacy leakage, in this paper, we first propose a proxy source node selection mechanism by constructing the candidate region. Secondly, based on the residual energy of the node, we propose a shortest routing algorithm to achieve better forwarding efficiency. Finally, by combining the proposed proxy source node selection mechanism with the proposed shortest routing algorithm based on the residual energy, we further propose a new, anonymous communication scheme. Meanwhile, the performance analysis indicates that the anonymous communication scheme can effectively protect the location privacy of the source nodes and reduce the network overhead.

Perpetrators leverage the untraceable feature to conduct illegal behaviors leading security issues with respect to mixing coins. Generally, bad coins are blocked based on a common blacklist. However, the blacklist may not be updated in time, which results in that bad coins escape the blocking. Consequently, perpetrators can still conduct illicit behaviors such as money laundering. In this paper, we apply game theory under imperfect information to study how coins' quality restrain these illicit behaviors under the incomplete scenario. More specifically, we propose a strategy for participants to submit deposits if they hope to mix coins with others even if they are not in blacklist at this time. The deposits will not be refunded when participants are included in the blacklist after mixing. Therefore, no participants have incentives to mix with bad coins. At the last part of this paper, we also simulate the incomes for participants, which indicates that deposits strategy is effective to prevent illicit behaviors.

The vulnerabilities in cryptographic currencies facilitate the adversarial attacks. Therefore, the attackers have incentives to increase their rewards by strategic behaviors. Block withholding attacks (BWH) are such behaviors that attackers withhold blocks in the target pools to subvert the blockchain ecosystem. Furthermore, BWH attacks may dwarf the countermeasures by combining with selfish mining attacks or other strategic behaviors, for example, fork after withholding (FAW) attacks and power adaptive withholding (PAW) attacks. That is, the attackers may be intelligent enough such that they can dynamically gear their behaviors to optimal attacking strategies. In this paper, we propose mixed-BWH attacks with respect to intelligent attackers, who leverage reinforcement learning to pin down optimal strategic behaviors to maximize their rewards. More specifically, the intelligent attackers strategically toggle among BWH, FAW, and PAW attacks. Their main target is to fine-tune the optimal behaviors, which incur maximal rewards. The attackers pinpoint the optimal attacking actions with reinforcement learning, which is formalized into a Markov decision process. The simulation results show that the rewards of the mixed strategy are much higher than that of honest strategy for the attackers. Therefore, the attackers have enough incentives to adopt the mixed strategy.

Blockchain is a “decentralized” system, where the security heavily depends on that of the consensus protocols. For instance, attackers gain illegal revenues by leveraging the vulnerabilities of the consensus protocols. Such attacks consist of selfish mining (SM1), optimal selfish mining ( ϵ-optimal), bribery selfish mining (BSM), and so forth. In existing works, the attacks only consider the circumstances, where part of miners are rational. However, miners are hardly nonrational in the blockchain system since they hope to maximize their revenues. Furthermore, attackers prefer intelligent tools to increase their power for more additional revenues. Therefore, new models are urgently needed to formulate the scenarios, where attackers are purely rational and intelligent. In this paper, we propose a new BSM model, where all miners are rational. Moreover, rational attackers are intelligent such that they optimize their strategies by utilizing reinforcement learning to boost their revenues. More specifically, we propose a new selfish mining algorithm: intelligent bribery selfish mining (IPBSM), where attackers choose optimal strategies resorting to reinforcement learning when they interact with the external environment. The external environment can be further modeled as a Markov decision process to facilitate the construction of reinforcement learning. The simulation results manifest that IPBSM, compared with SM1 and ϵ-optimal, has lower power thresholds and higher revenues. Therefore, IPBSM is a threat no to be neglected to the blockchain system.

Compared with several typical field-sequential stereoscopic TV systems, a flicker-free field- sequential stereoscopic TV system compatible with a current PAL system is proposed. Except the frame memory technique, resetting composite sync signal and line interpolation techniques are adopted in this system, therefore, it has the advantage of fine and smooth scanning structure, high vertical resolution, and easy application.