The wide spreading of Internet of Things (IoT) sensors generates vast spatio-temporal data streams, but ensuring data credibility is a critical yet unsolved challenge for applications like smart homes. While spatio-temporal graph (STG) models are a leading paradigm for such data, they often fall short in dynamic, human-centric environments due to two fundamental limitations: (1) their reliance on static graph topologies, which fail to capture physical, event-driven dynamics, and (2) their tendency to confuse spurious correlations with true causality, undermining robustness in human-centric environments. To address these gaps, we propose the Dynamic Causal Spatio-Temporal Graph Network (DyC-STG), a novel framework designed for real-time data credibility analysis in IoT. Our framework features two synergistic contributions: an event-driven dynamic graph module that adapts the graph topology in real-time to reflect physical state changes, and a causal reasoning module to distill causally-aware representations by strictly enforcing temporal precedence. To facilitate the research in this domain we release two new real-world datasets. Comprehensive experiments show that DyC-STG establishes a new state-of-the-art, outperforming the strongest baselines by 1.4 percentage points and achieving an F1-Score of up to 0.930.

Federated learning (FL) enables collaborative model training across distributed nodes without exposing raw data, but its decentralized nature makes it vulnerable in trust-deficient environments. Inference attacks may recover sensitive information from gradient updates, while poisoning attacks can degrade model performance or induce malicious behaviors. Existing defenses often suffer from high communication and computation costs, or limited detection precision. To address these issues, we propose LSHFed, a robust and communication-efficient FL framework that simultaneously enhances aggregation robustness and privacy preservation. At its core, LSHFed incorporates LSHGM, a novel gradient verification mechanism that projects high-dimensional gradients into compact binary representations via multi-hyperplane locality-sensitive hashing. This enables accurate detection and filtering of malicious gradients using only their irreversible hash forms, thus mitigating privacy leakage risks and substantially reducing transmission overhead. Extensive experiments demonstrate that LSHFed maintains high model performance even when up to 50% of participants are collusive adversaries, while achieving up to a 1000× reduction in gradient verification communication compared to full-gradient methods.

Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are rife with missing views and continuously emerging classes, which pose significant obstacles to robust learning. Prevailing methods are ill-equipped for this reality, as they either lack adaptability to new classes or incur exponential parameter growth when handling all possible missing-view patterns, severely limiting their scalability in web environments. To systematically address this gap, we formally introduce a novel task, termed incomplete multi-view multi-label class incremental learning (IMvMLCIL), which requires models to simultaneously address heterogeneous missing views and dynamic class expansion. To tackle this task, we propose E2PL, an Effective and Efficient Prompt Learning framework for IMvMLCIL. E2PL unifies two novel prompt designs: task-tailored prompts for class-incremental adaptation and missing-aware prompts for the flexible integration of arbitrary view-missing scenarios. To fundamentally address the exponential parameter explosion inherent in missing-aware prompts, we devise an efficient prototype tensorization module, which leverages atomic tensor decomposition to elegantly reduce the prompt parameter complexity from exponential to linear w.r.t. the number of views. We further incorporate a dynamic contrastive learning strategy explicitly model the complex dependencies among diverse missing-view patterns, thus enhancing the model's robustness. Extensive experiments on three benchmarks demonstrate that E2PL consistently outperforms state-of-the-art methods in both effectiveness and efficiency. The codes and datasets are available at https://anonymous.4open.science/r/code-for-E2PL.

Reinforcement learning-based recommendation systems (RLRS) are increasingly favored for their ability to leverage online interactive feedback, enabling adaptive and personalized decision-making. In this setting, user feedback serves as both a behavioral signal and an optimization target, making it essential for policy learning. However, collecting such feedback, e.g., clicks, ratings, and engagement traces, raises serious privacy concerns, posing critical challenges for value estimation, online adaptation, and privacy protection. In this paper, we propose FeedGuard, a critic-guided reinforcement learning framework with privacy-preserving feedback. FeedGuard enhances trajectory modeling via critic guidance, enables joint online fine-tuning with effective exploration–exploitation tradeoffs, and enforces end-to-end privacy protection across the feedback lifecycle via split federated learning and differential privacy. We further provide a formal analysis of its differential privacy guarantees. Extensive experiments on four public recommendation datasets and the VirtualTB platform show that FeedGuard performs well in both offline and online settings, while maintaining rigorous privacy guarantees with minimal degradation.

Memory disaggregation has become a research trend in data centers. Existing studies fall into two paths: Network-based logical memory disaggregation (LMD) and Compute Express Link (CXL)-based physical memory disaggregation (PMD). However, LMD suffers from network overhead, while PMD incurs expensive hardware costs and lacks flexibility. This paper advocates for building LMD systems on shared CXL memory, taking advantage of its low latency and cache-coherent memory access. However, shared CXL memory has severe scalability issues due to its strict coherence model.This paper introduces Swarm, an efficient LMP system built on shared CXL memory. Swarm divides shared CXL memory into small cacheable memory and large non-cacheable memory. Hardware only needs to maintain coherence for cacheable memory, while software handles coherence for non-cacheable memory, thereby enabling all CXL memory to be shared. Then, Swarm implements cross-node RPC and dynamic global memory allocation on shared CXL memory to improve performance and memory utilization. Swarm also proposes distributed computing offloading to fully leverage compute power on memory nodes for acceleration. Our evaluation shows that Swarm not only improves the throughput (e.g., by 3.6× and 1.8×) compared with representative network-based LMD system, AIFM and CXL-based PMD system when computing offloading is enabled but also achieves an advantage in TCO.

Loan default prediction is a crucial component of risk management in financial loan services. In practice, significant monetary losses often stem from initially creditworthy loans that later default unexpectedly. This phenomenon arises because such loans, while assessed as low-risk at disbursement initially, have a high default-intention to arise, in a delayed manner, at an indeterminate time during the repayment period after disbursement. We term such default-intention as Delayed Defaultintention. In this paper, we present a new and pressing task, namely, Delayed Default-intention based Default prediction ($\mathrm{D}^{3}$), which is of practical significance but has been rarely studied in prior research. The core challenge of$D^{3}$task lies in its farsighted inference of delayed default-intention, as it does not manifest immediately after disbursement. To address this, we propose a survival analysis framework for the$\mathrm{D}^{3}$task and a novel RW-D${ }^{3}$method, which models the repayment willingness (RW) in a loan as a negatively correlated alternative to delayed default-intention. RW-D${ }^{3}$systematically initializes, dynamizes, and recovers the original RW representations based on user behavior sequences, enhancing their predictive capacity from a short-term to a long-term perspective. Additionally, RW-D${ }^{3}$provides a comprehensive prediction of defaults triggered by delayed default-intention by jointly considering repayment status and timing. Extensive experiments demonstrate the superiority of RW-D${ }^{3}$over state-of-the-art methods in both its predictive effectiveness and explainability in financial loan services.

Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. However, the class-imbalanced data distribution in real-world scenarios poses great challenges to SSL, resulting in performance degradation. Existing class-imbalanced semi-supervised learning (CISSL) methods mainly focus on rebalancing datasets but ignore the potential of using hard examples to enhance performance, making it difficult to fully harness the power of unlabeled data even with sophisticated algorithms. To address this issue, we propose a method that enhances the performance of Imbalanced Semi-Supervised Learning by Mining Hard Examples (SeMi). This method distinguishes the entropy differences among logits of hard and easy examples, thereby identifying hard examples and increasing the utility of unlabeled data, better addressing the imbalance problem in CISSL. In addition, we maintain a class-balanced memory bank with confidence decay for storing high-confidence embeddings to enhance the pseudo-labels' reliability. Although our method is simple, it is effective and seamlessly integrates with existing approaches. We perform comprehensive experiments on standard CISSL benchmarks and experimentally demonstrate that our proposed SeMi outperforms existing state-of-the-art methods on multiple benchmarks, especially in reversed scenarios, where our best result shows approximately a 54.8% improvement over the baseline methods. Our code is available at https://github.com/pywin/SeMi.

Recent advancements in optimization-based text-to-3D generation heavily rely on distilling knowledge from pre-trained text-to-image diffusion models using techniques like Score Distillation Sampling (SDS), which often introduce artifacts such as over-saturation and over-smoothing into the generated 3D assets. In this paper, we address this essential problem by formulating the generation process as learning an optimal, direct transport trajectory between the distribution of the current rendering and the desired target distribution, thereby enabling high-quality generation with smaller Classifier-free Guidance (CFG) values. At first, we theoretically establish SDS as a simplified instance of the Schrödinger Bridge framework. We prove that SDS employs the reverse process of an Schrödinger Bridge, which, under specific conditions (e.g., a Gaussian noise as one end), collapses to SDS's score function of the pre-trained diffusion model. Based upon this, we introduce Trajectory-Centric Distillation (TraCe), a novel text-to-3D generation framework, which reformulates the mathematically trackable framework of Schrödinger Bridge to explicitly construct a diffusion bridge from the current rendering to its text-conditioned, denoised target, and trains a LoRA-adapted model on this trajectory's score dynamics for robust 3D optimization. Comprehensive experiments demonstrate that TraCe consistently achieves superior quality and fidelity to state-of-the-art techniques. Our code will be released to the community.

Snapshot technology is crucial for data protection and system recovery in virtualized environments, particularly with the growing need for continuous snapshots to maintain the integrity of long-running web-based and distributed applications. However, traditional snapshot methods often suffer from performance bottlenecks, and inefficient storage usage. These challenges are closely tied to the way memory pages are accessed during VM execution, where memory access patterns show significant disparities between frequently accessed "hot" pages and less-used "cold" pages. In this paper, we introduce HeatSnap, a continuous snapshot system designed to address these issues by leveraging the uneven access frequencies of memory pages. HeatSnap distinguishes between intensive hot pages and dirty pages, applying specialized snapshotting and storage strategies to optimize the handling of both hot and cold memory regions. This approach aims to optimize snapshot efficiency, minimize performance impact on the VM, and decrease storage costs. Our implementation of HeatSnap on QEMU/KVM demonstrates significant improvements in VM performance loss, snapshot duration, and storage efficiency compared to existing methods, as evidenced by evaluations on common web and cloud-based workloads.

Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SP) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness, as the integrity of query results depends on the SPs' trustworthiness. Although ADS can verify the integrity of individual data points, they fall short of preventing SPs from omitting valid results. In this paper, we delineate the fundamental distinctions between data retrieval in blockchains and traditional database systems. Drawing upon these insights, we introduce the BPI framework, which employs a suite of validation models that ascertain the inclusion of all valid content in retrieval outcomes, with low overhead. We further present ''Articulated Search'', a query pattern specifically tailored for blockchain environments, which not only enhances retrieval efficiency but also substantially reduces costs during data user updates. Extensive experimental evaluations demonstrate that the BPI framework achieves outstanding scalability and performance in keyword searches within blockchain environments, surpassing EthMB+ and state-of-the-art search databases commonly used in mainstream hybrid storage blockchains (HSB). Notably, the Articulated Search pattern improves query performance by over three orders of magnitude, highlighting its potential as a transformative approach to blockchain query optimization.

Digital service networks often face the challenge ofService-OrientedFairness (SOF), where service nodes with varying levels of activity may receive unequal treatment. This article takes the recommendation service system as a representative case to explore and mitigate the impact of SOF. The SOF issue in the recommendation service system can be abstracted asUser-OrientedFairness (UOF), where service models often exhibit bias toward a small group of users, resulting in significant unfairness in the quality of recommendations. Existing research on UOF faces three major limitations, and no single approach effectively addresses all of them.Limitation 1:Post-processing methods fail to address the root cause of the UOF issue.Limitation 2:Some in-processing methods rely heavily on unstable user similarity calculations under severe data sparsity problems.Limitation 3:Other in-processing methods overlook the disparate treatment of individual users within user groups. In this article, we propose a novelIndividualReweighting forUser-OrientedFairness framework, namely IR-UOF, to address all the aforementioned limitations. The motivation behind IR-UOF is tointroduce an in-processing strategy that addresses the UOF issue at the individual level without the need to explore user similarities.We first conduct extensive experiments on three real-world recommendation service datasets using four backbone recommendation models to demonstrate the effectiveness of IR-UOF in mitigating UOF and improving recommendation fairness. Furthermore, we select two general digital service datasets to prove that IR-UOF can be extended to tackle the general SOF issue in other types of digital service networks. In summary, the IR-UOF framework achieves optimal model performance across all datasets, while improving fairness by at least 3.8% in recommendation systems and 24.7% in general service systems.

Computing ecosystems are shifting toward a computing continuum paradigm designed to handle the diverse and dynamic nature of computing resources spread across various locations. It demonstrates significant potential in providing high-bandwidth and low-latency services for users. However, as a large number of users request services from distributed computing continuum systems, it is critical to schedule numerous delay-sensitive, fractional workloads and maximum parallelism-bound jobs to appropriate backend resources,e.g., cloud container instances. In addition, the scheduling strategy also needs to maximize the social welfare that incorporates the utilities of jobs and the revenue of service providers. However, current workload scheduling algorithms are based on simple heuristics and lack performance guarantees. Due to the unpredictability of online requests, the distribution of requests should not be assumed. Therefore, designing an online workload scheduling strategy without assumptions on request distributions is essential for balancing the online workload. This work first establishes a spatiotemporal integrated resource pool to reflect the computational resources provided by distributed computing continuum systems. Then, several pseudo-social welfare functions and marginal cost functions are constructed, where the latter is used to estimate the marginal cost of provisioning services to each newly arrived job based on the current resource surplus. We propose an online workload scheduling strategy namedOnSocMaxto solve the above problems. It operates by following the solutions to several convex pseudo-social welfare maximization problems and is proven to be$\alpha$-competitive for some$\alpha$with a value of at least 2. The evaluation results demonstrate thatOnSocMaxoutperforms several benchmark strategies in maximizing social welfare.

In large-scale digital service sharing scenarios, given the large number of participating users, frequent cross-domain service interactions, and high-frequency service transactions, to ensure the trustworthiness of digital services, the architecture of the digital service sharing system usually chooses blockchain as its technological foundation. This not only ensures the security and credible deposit of data, but also achieves the credible traceability of data. However, in the current blockchain-based notarization architecture, there may be potential privacy leakage during data transmission, and users are unable to choose the encryption level of their data for blockchain deposition according to their own needs. Moreover, the underlying databases in current applications using blockchain lack convenient retrieval and categorization functionalities. In this work, we propose a trustworthy blockchain solution based on permission management, which implements hierarchical encryption and enables users to flexibly encrypt data according to their needs. Additionally, through the Double Star storage system, while ensuring the reliability of the system, we have also greatly improved the efficiency of data retrieval and classification. Compared to blockchain platforms like XRP and EOS, our solution achieves superior data retrieval and classification efficiency while implementing layered encryption.

Blockchain and smart contracts are one of the key technologies promoting Web 3.0. However, due to security considerations and consistency requirements, smart contracts currently only support simple and deterministic programs, which significantly hinders their deployment in intelligent Web 3.0 applications. To enhance smart contracts intelligence on the blockchain, we propose SMART, a plug-in smart contract framework that supports efficient AI model inference while being compatible with existing blockchains. To handle the high complexity of model inference, we propose an on-chain and off-chain joint execution model, which separates the SMART contract into two parts: the deterministic code still runs inside an on-chain virtual machine, while the complex model inference is offloaded to off-chain compute nodes. To solve the non-determinism brought by model inference, we leverage Trusted Execution Environments (TEEs) to endorse the integrity and correctness of the off-chain execution. We also design distributed attestation and secret key provisioning schemes to further enhance the system security and model privacy. We implement a SMART prototype and evaluate it on a popular Ethereum Virtual Machine (EVM)-based blockchain. Theoretical analysis and prototype evaluation show that SMART not only achieves the security goals of correctness, liveness, and model privacy, but also has approximately 5 orders of magnitude faster inference efficiency than existing on-chain solutions.

With the emergence of the sixth-generation (6G) communication technologies, the Internet of Vehicles (IoV) is rapidly developing with the coordination between intelligent networked vehicles, road infrastructures, and the cloud. However, the openness and dynamic nature of the IoV raise significant security and privacy concerns, highlighting the need for efficient authentication schemes. Conventional authentication schemes are no longer suitable for 6G-enabled IoV due to high latency, single point of failure, and heavy management costs. Additionally, existing literature on multi-domain authentication mainly investigates vehicle mobility, ignoring the challenges posed by vehicle heterogeneity. To fill this gap, we propose a multi-domain authentication scheme with conditional privacy preservation (MACPP) that considers administrative domains (AD) and geographic domains (GD) in the IoV. In MACPP, we design a novel identity-based signature scheme without requiring bilinear pairing for efficient authentication. Additionally, we propose a blockchain-assisted pseudonym management scheme (BAPM) to further improve system security by designing a dynamical sparse Merkle tree structure (DSMT). We demonstrate that the proposed MACPP satisfies the security requirements through an in-depth security analysis. Moreover, the experimental results demonstrate the effectiveness and efficiency of both MACPP and BAPM.

The property of Internet of Things (IoT) applications is their capability to execute tasks through the collaboration of interconnected IoT objects. However, IoT collaborations face significant challenges due to security threats that undermine their reliability. An uncertified task publisher may deceive IoT devices into executing illegal tasks, while malicious attackers may intercept and modify transmitted data. Existing works on IoT trusted management issues tend to concentrate on individual aspects, such as authentication, privacy protection, and access control. However, trusted management for IoT collaboration is a multifaceted and intricate endeavor that necessitates a comprehensive approach. To fill this gap, we propose a lightweight authentication-driven trusted management framework that includes a novel authentication and key agreement scheme to guarantee the validity of task publishers, with greatly reduced overheads compared to recent works. The framework also incorporates a distributed data storage scheme and a fine-grained access control mechanism. We record the interactive messages on the blockchain to ensure behavior traceability. We evaluate the authentication scheme through comparative experiments and formal security analysis, demonstrating its efficiency and effectiveness. The experimental results of data storage and acquisition in real-world IoT environments indicate that the proposed framework is a feasible solution for reliable IoT collaboration.

Hundreds of thousands of Java applications have been deployed in data centers at our production Cloud to support burst peak traffic. However, detecting performance bugs in Java can be difficult as they may not prevent the applications from running correctly, and may not even manifest at low loads. Profiling data collected from production provides insight into the actual running states of applications. In this paper, we aim to identify hot spots for further performance debugging by analyzing profiling data from tens of thousands of machines in the data center using a module-based approach. We present our practical experience with module classification, which allows for filtering of out-of-range modules and long-duration modules of high utilization. Our study proposes a heuristic solution to detect performance bugs in Java at scale.

Summary Performance evaluation is the basis for choosing appropriate system‐level configurations for large‐scale data centers. While the change of a system‐level configuration would impact lots of jobs in the data centers, traditional load‐testing benchmarks are not sufficient to support the decision‐making because they cannot accurately reproduce the complex behaviors of a large number of jobs. Therefore, we expect to further evaluate the system configuration based on the production environment. However, there are technical challenges, namely, the lack of a holistic evaluation method that can unite the evaluation results of various jobs, and the uninterruptable production environment that should not be affected by the evaluation procedure. To address these challenges, we propose a holistic performance evaluation methodology and design its implementation platform. We introduce a simple but powerful performance metric, ERU (effectiveness of resource usage), and combine the ERU of involved jobs into a summarized value to measure the effect of a configuration change. We validate our ERU metric by comparing it with the CPI (Cycle per Instruction) and QPS (query per second) metrics, deploy the platform to production data centers and demonstrate the effectiveness for measuring system‐level configurations of both software (JVM compiler update) and hardware (NUMA on/off) to save 14.44% and 11% resources respectively in advance.

Abstract Modern Cloud applications have evolved from monolithic systems to numerous distributed microservices whose workflows interact via Remote Procedure Calls (RPCs). The benchmarks focusing on individual microservice components are insufficient because the reference relationships between components, which form the microservice workflow structure, are another critical aspect of microservice applications. Unfortunately, understanding the characteristics of microservice workflow in the production Cloud remains a missing piece in the literature, which prevents the representative of microservice benchmarks. In this paper, we fill this gap by characterizing and synthesizing the microservice workflows based on the trace data of the Toutiao application that is running on ByteDance Cloud. We examine the microservice workflows starting from DAG graphs, introduce observed properties that are easy to ignore but important, show the artificiality of the workflow by statistic description, and explore the high cost of network overhead. We further synthesize the workflow following the characteristics observed. The extensive evaluations show that the synthesized microservice workflows have consistent statistical characteristics as the production ones. A case study applying the synthesized workflows proves its usability.

With the ever-increasing requirements of delay-sensitive and mission-critical applications, it becomes a popular research trend to incorporate edge computing in the Internet of Things (IoT) to mitigate the pressure of traditional cloud-based IoT architecture. Edge computing delivers real-time computations and communications for IoT devices by leveraging edge servers deployed close to users, which creates a collaborative edge computing (CEC) paradigm. The capacity of edge servers is beneficial but risky, as vulnerable servers can be exploited to conduct surveillance or perform other nefarious activities. Besides, fake IoT devices would bring security threats and compromise the IoT system. This highlights the necessity of designing a secure and efficient mutual authentication scheme for CEC. In this direction, related works have proposed various authentication mechanisms, but most of them are found unfit due to the absence of decentralization, anonymity, and mobility. Motivated by this fact, we propose a blockchain-based mutual authentication scheme that bridges these gaps. Specifically, blockchain, certificateless cryptography, elliptic curve cryptography, and pseudonym-based cryptography are integrated into our scheme to provide mutual authentication between edge servers and IoT devices. Except for static conditions, both intraedge and interedge authentication are considered. Besides, we elaborate on the key generation procedures and design a session key negotiation mechanism. Extensive experiments and security analyses have been conducted to show the feasibility of the proposed scheme.

Blockchain is regarded as one of the most promising technologies to upgrade e-commerce. This article analyzes the challenges that current e-commerce is facing and introduces a new scenario of e-commerce enabled by blockchain. A framework is proposed for mining tasks in this scenario offloaded onto edge servers based on mobile edge computing. Then, the offloading issue is modeled as a multi-constrained optimization problem, and evolutionary algorithms are utilized and re-designed as solvers. The experimental results validate the efficiency of the framework and algorithms and also show that the lower bound of computation resources exists to obtain the maximum overall revenue.

The prosperous development of IoT has created tremendous opportunities to improve people's lives. Essentially, the core property of the IoT applications is the ability to perform collaborative tasks with data supplied by separate IoT managers. However, the fulfillment of collaborative tasks is driven by the participations of the IoT managers. Generally, the willingness can be activated with appropriate profit (or incentive cost). Thus, an efficient incentive mechanism is needed to motivate the IoT managers to participate in the collaboration. In this paper, we present a reverse auction-based incentive mechanism with the goal of minimizing and stabilizing incentive costs while maintaining adequate participants. To prevent the incentive cost explosion, a droppers recruiting scheme is leveraged to attract the inactive participants. A price verification strategy is designed to avoid bid cheating. Furthermore, we introduce blockchain to orchestrate the interactions between collaborative parties, so as to protect their privacy. Finally, we show the feasibility and efficiency of our proposed framework with simulation experiments and theoretical analysis.